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\n  \n 2025\n \n \n (20)\n \n \n
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\n \n\n \n \n \n \n \n Less is More: Multimodal Region Representation via Pairwise Inter-view Learning.\n \n \n \n\n\n \n Namgung, M.; Lin, Y.; Lee, J.; and Chiang, Y.\n\n\n \n\n\n\n arXiv preprint arXiv:2505.18178. 2025.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{namgung2025less,\n  title={Less is More: Multimodal Region Representation via Pairwise Inter-view Learning},\n  author={Namgung, Min and Lin, Yijun and Lee, JangHyeon and Chiang, Yao-Yi},\n  journal={arXiv preprint arXiv:2505.18178},\n  year={2025}\n}\n
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\n \n\n \n \n \n \n \n \n MapQA: Open-domain Geospatial Question Answering on Map Data.\n \n \n \n \n\n\n \n Li, Z.; Grossman, M.; Qasemi, E.; Kulkarni, M.; Chen, M.; and Chiang, Y.\n\n\n \n\n\n\n arXiv preprint arXiv:2503.07871. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"MapQA:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{li2025mapqa,\n  title={MapQA: Open-domain Geospatial Question Answering on Map Data},\n  author={Li, Zekun and Grossman, Malcolm and Qasemi, Eric and Kulkarni, Mihir and Chen, Muhao and Chiang, Yao-Yi},\n  journal={arXiv preprint arXiv:2503.07871},\n  year={2025},\n  url={https://arxiv.org/abs/2503.07871},\n  abstract={Geospatial question answering (QA) is a fundamental task in navigation and point of interest (POI) searches. While existing geospatial QA datasets exist, they are limited in both scale and diversity, often relying solely on textual descriptions of geo-entities without considering their geometries. A major challenge in scaling geospatial QA datasets for reasoning lies in the complexity of geospatial relationships, which require integrating spatial structures, topological dependencies, and multi-hop reasoning capabilities that most text-based QA datasets lack. To address these limitations, we introduce MapQA, a novel dataset that not only provides question-answer pairs but also includes the geometries of geo-entities referenced in the questions. MapQA is constructed using SQL query templates to extract question-answer pairs from OpenStreetMap (OSM) for two study regions: Southern California and Illinois. It consists of 3,154 QA pairs spanning nine question types that require geospatial reasoning, such as neighborhood inference and geo-entity type identification. Compared to existing datasets, MapQA expands both the number and diversity of geospatial question types. We explore two approaches to tackle this challenge: (1) a retrieval-based language model that ranks candidate geo-entities by embedding similarity, and (2) a large language model (LLM) that generates SQL queries from natural language questions and geo-entity attributes, which are then executed against an OSM database. Our findings indicate that retrieval-based methods effectively capture concepts like closeness and direction but struggle with questions that require explicit computations (e.g., distance calculations). LLMs (e.g., GPT and Gemini) excel at generating SQL queries for one-hop reasoning but face challenges with multi-hop reasoning, highlighting a key bottleneck in advancing geospatial QA systems.}\n}\n\n
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\n Geospatial question answering (QA) is a fundamental task in navigation and point of interest (POI) searches. While existing geospatial QA datasets exist, they are limited in both scale and diversity, often relying solely on textual descriptions of geo-entities without considering their geometries. A major challenge in scaling geospatial QA datasets for reasoning lies in the complexity of geospatial relationships, which require integrating spatial structures, topological dependencies, and multi-hop reasoning capabilities that most text-based QA datasets lack. To address these limitations, we introduce MapQA, a novel dataset that not only provides question-answer pairs but also includes the geometries of geo-entities referenced in the questions. MapQA is constructed using SQL query templates to extract question-answer pairs from OpenStreetMap (OSM) for two study regions: Southern California and Illinois. It consists of 3,154 QA pairs spanning nine question types that require geospatial reasoning, such as neighborhood inference and geo-entity type identification. Compared to existing datasets, MapQA expands both the number and diversity of geospatial question types. We explore two approaches to tackle this challenge: (1) a retrieval-based language model that ranks candidate geo-entities by embedding similarity, and (2) a large language model (LLM) that generates SQL queries from natural language questions and geo-entity attributes, which are then executed against an OSM database. Our findings indicate that retrieval-based methods effectively capture concepts like closeness and direction but struggle with questions that require explicit computations (e.g., distance calculations). LLMs (e.g., GPT and Gemini) excel at generating SQL queries for one-hop reasoning but face challenges with multi-hop reasoning, highlighting a key bottleneck in advancing geospatial QA systems.\n
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\n \n\n \n \n \n \n \n \n Towards the next generation of Geospatial Artificial Intelligence.\n \n \n \n \n\n\n \n Mai, G.; Xie, Y.; Jia, X.; Lao, N.; Rao, J.; Zhu, Q.; Liu, Z.; Chiang, Y.; and Jiao, J.\n\n\n \n\n\n\n International Journal of Applied Earth Observation and Geoinformation, 136: 104368. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{MAI2025104368,\n  title = {Towards the next generation of Geospatial Artificial Intelligence},\n  journal = {International Journal of Applied Earth Observation and Geoinformation},\n  volume = {136},\n  pages = {104368},\n  year = {2025},\n  issn = {1569-8432},\n  doi = {https://doi.org/10.1016/j.jag.2025.104368},\n  url = {https://www.sciencedirect.com/science/article/pii/S1569843225000159},\n  author = {Gengchen Mai and Yiqun Xie and Xiaowei Jia and Ni Lao and Jinmeng Rao and Qing Zhu and Zeping Liu and Yao-Yi Chiang and Junfeng Jiao},\n  keywords = {Geospatial Artificial Intelligence, Heterogeneity-aware GeoAI, Knowledge-Guided GeoAI, Spatial representation learning, Geo-Foundation Models, Fairness-aware GeoAI, Privacy-aware GeoAI, Interpretable and explainable GeoAI},\n  abstract = {Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.}\n}\n\n
\n
\n\n\n
\n Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.\n
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\n \n\n \n \n \n \n \n \n HiCoTraj: Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory.\n \n \n \n \n\n\n \n Xie, J.; Jiao, Y.; Kim, J.; Chiang, Y.; Zhao, L.; and Shafique, K.\n\n\n \n\n\n\n In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Generative and Agentic AI for Multi-Modality Space-Time Intelligence, of GeoGenAgent '25, pages 49–53, New York, NY, USA, 2025. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"HiCoTraj:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764915.3770723,\n  author = {Xie, Junyi and Jiao, Yuankun and Kim, Jina and Chiang, Yao-Yi and Zhao, Lingyi and Shafique, Khurram},\n  title = {HiCoTraj: Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory},\n  year = {2025},\n  isbn = {9798400722615},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3764915.3770723},\n  doi = {10.1145/3764915.3770723},\n  abstract = {Inferring demographic attributes such as age, sex, or income level from human mobility patterns enables critical applications such as targeted public health interventions, equitable urban planning, and personalized transportation services. Existing mobility-based demographic inference studies heavily rely on large-scale trajectory data with demographic labels, leading to limited interpretability and poor generalizability across different datasets and user groups. We propose HiCoTraj (Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory), a framework that leverages LLMs' zero-shot learning and semantic understanding capabilities to perform demographic inference without labeled training data. HiCoTraj transforms trajectories into semantically rich, natural language representations by creating detailed activity chronicles and multi-scale visiting summaries. Then HiCoTraj uses a novel hierarchical chain of thought reasoning to systematically guide LLMs through three cognitive stages: factual feature extraction, behavioral pattern analysis, and demographic inference with structured output. This approach addresses the scarcity challenge of labeled demographic data while providing transparent reasoning chains. Experimental evaluation on real-world trajectory data demonstrates that HiCoTraj achieves competitive performance across multiple demographic attributes in zero-shot scenarios.},\n  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Generative and Agentic AI for Multi-Modality Space-Time Intelligence},\n  pages = {49–53},\n  numpages = {5},\n  keywords = {demographic inference, large language model reasoning, trajectory analysis, chain-of-thought prompting, zero-shot learning},\n  location = {The Graduate Hotel Minneapolis, Minneapolis, MN, USA},\n  series = {GeoGenAgent '25}\n}\n\n
\n
\n\n\n
\n Inferring demographic attributes such as age, sex, or income level from human mobility patterns enables critical applications such as targeted public health interventions, equitable urban planning, and personalized transportation services. Existing mobility-based demographic inference studies heavily rely on large-scale trajectory data with demographic labels, leading to limited interpretability and poor generalizability across different datasets and user groups. We propose HiCoTraj (Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory), a framework that leverages LLMs' zero-shot learning and semantic understanding capabilities to perform demographic inference without labeled training data. HiCoTraj transforms trajectories into semantically rich, natural language representations by creating detailed activity chronicles and multi-scale visiting summaries. Then HiCoTraj uses a novel hierarchical chain of thought reasoning to systematically guide LLMs through three cognitive stages: factual feature extraction, behavioral pattern analysis, and demographic inference with structured output. This approach addresses the scarcity challenge of labeled demographic data while providing transparent reasoning chains. Experimental evaluation on real-world trajectory data demonstrates that HiCoTraj achieves competitive performance across multiple demographic attributes in zero-shot scenarios.\n
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\n \n\n \n \n \n \n \n \n Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning.\n \n \n \n \n\n\n \n Jelinski, N. A; Chiang, Y.; Nawrocki, T.; Macander, M.; Ives, S.; Grunwald, S.; Brungard, C.; Chen, T.; and Lin, Y.\n\n\n \n\n\n\n In Proceedings of ACM SIGSPATIAL 2025, 2025. \n \n\n\n\n
\n\n\n\n \n \n \"Fine-ScalePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{jelinski2025fine,\n  title = {Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning},\n  author = {Jelinski, Nicolas A and Chiang, Yao-Yi and Nawrocki, Timm and Macander, Matt and Ives, Sue and Grunwald, Sabine and Brungard, Colby and Chen, Theresa and Lin, Yijun},\n  year = {2025},\n  booktitle = {Proceedings of ACM SIGSPATIAL 2025},\n  url = {https://sigspatial2025.sigspatial.org/application-accepted/}\n}\n\n
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\n \n\n \n \n \n \n \n \n StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery.\n \n \n \n \n\n\n \n Kim, J.; Jang, L.; Chiang, Y.; Wang, G.; and Pasco, M.\n\n\n \n\n\n\n arXiv preprint arXiv:2506.14670. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"StreetLens:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{kim2025streetlens,\n  title = {StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery},\n  author = {Kim, Jina and Jang, Leeje and Chiang, Yao-Yi and Wang, Guanyu and Pasco, Michelle},\n  journal = {arXiv preprint arXiv:2506.14670},\n  year = {2025},\n  doi = {10.48550/arXiv.2506.14670},\n  url = {https://arxiv.org/abs/2506.14670}\n}\n\n
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\n \n\n \n \n \n \n \n \n Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning.\n \n \n \n \n\n\n \n Namgung, M.; Lee, J.; Ding, F.; and Chiang, Y.\n\n\n \n\n\n\n arXiv preprint arXiv:2506.15113. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"TransitPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{namgung2025transit,\n  title = {Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning},\n  author = {Namgung, Min and Lee, JangHyeon and Ding, Fangyi and Chiang, Yao-Yi},\n  journal = {arXiv preprint arXiv:2506.15113},\n  year = {2025},\n  doi = {10.48550/arXiv.2506.15113},\n  url = {https://arxiv.org/abs/2506.15113}\n}\n\n
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\n \n\n \n \n \n \n \n \n DIGMAPPER: A Modular System for Automated Geologic Map Digitization.\n \n \n \n \n\n\n \n Duan, W.; Gerlek, M.; Minton, S.; Knoblock, C.; Lin, F.; Chen, T.; Jang, L.; Kirsanova, S.; Li, Z.; Lin, Y.; and Chiang, Y.\n\n\n \n\n\n\n arXiv preprint arXiv:2506.16006. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"DIGMAPPER:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{duan2025digmapper,\n  title = {DIGMAPPER: A Modular System for Automated Geologic Map Digitization},\n  author = {Duan, Weiwei and Gerlek, Michael and Minton, Steven and Knoblock, Craig and Lin, Fandel and Chen, Theresa and Jang, Leeje and Kirsanova, Sofia and Li, Zekun and Lin, Yijun and Chiang, Yao-Yi},\n  journal = {arXiv preprint arXiv:2506.16006},\n  year = {2025},\n  doi = {10.48550/arXiv.2506.16006},\n  url = {https://arxiv.org/abs/2506.16006}\n}\n\n
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\n \n\n \n \n \n \n \n \n GeoAnomaly Detection: Towards finding Needles of Anomalous Behavior in a Haystack of Geospatial Data.\n \n \n \n \n\n\n \n Chiang, Y.; Kim, J.; Krause, C.; Mattei, E.; Shafique, K.; Wenk, C.; and Züfle, A.\n\n\n \n\n\n\n SIGSPATIAL Special, 15(1): 9–15. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"GeoAnomalyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{10.1145/3757932.3757935,\n  title = {GeoAnomaly Detection: Towards finding Needles of Anomalous Behavior in a Haystack of Geospatial Data},\n  author = {Chiang, Yao-Yi and Kim, Joon-Seok and Krause, Cory and Mattei, Enrico and Shafique, Khurram and Wenk, Carola and Z\\"{u}fle, Andreas},\n  journal = {SIGSPATIAL Special},\n  year = {2025},\n  volume = {15},\n  number = {1},\n  pages = {9–15},\n  numpages = {7},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3757932.3757935},\n  doi = {10.1145/3757932.3757935},\n  abstract = {The task of anomaly detection in geospatial data is to find data points in space and time that deviate so much from other observations as to arouse suspicion that it was generated by a different mechanism. Most of the existing work in geospatial data analysis relies on supervised machine learning, which involves training models on labeled datasets, allowing them to learn and make accurate predictions. Anomalies, by definition, are rare events that occur infrequently and unpredictably. As a result, labeled datasets for these events are either non-existent or extremely limited. Consequently, anomaly detection research calls for new strategies that can identify rare and unexpected events based on patterns and distributions within the data itself in an unsupervised fashion. These anomalies may manifest as unexpected changes in environmental conditions, natural disasters, unusual human activities, or irregular patterns in spatiotemporal distributions. To answer this call, we organized the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection (GeoAnomalies'24) at SIGSPATIAL'24. In this Newsletter Article, we report our first findings and map future research directions.}\n}\n\n
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\n The task of anomaly detection in geospatial data is to find data points in space and time that deviate so much from other observations as to arouse suspicion that it was generated by a different mechanism. Most of the existing work in geospatial data analysis relies on supervised machine learning, which involves training models on labeled datasets, allowing them to learn and make accurate predictions. Anomalies, by definition, are rare events that occur infrequently and unpredictably. As a result, labeled datasets for these events are either non-existent or extremely limited. Consequently, anomaly detection research calls for new strategies that can identify rare and unexpected events based on patterns and distributions within the data itself in an unsupervised fashion. These anomalies may manifest as unexpected changes in environmental conditions, natural disasters, unusual human activities, or irregular patterns in spatiotemporal distributions. To answer this call, we organized the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection (GeoAnomalies'24) at SIGSPATIAL'24. In this Newsletter Article, we report our first findings and map future research directions.\n
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\n \n\n \n \n \n \n \n \n Augmenting Human-Centered Racial Covenant Detection and Georeferencing with Plug-and-Play NLP Pipelines.\n \n \n \n \n\n\n \n Pyo, J.; Jiao, Y.; Chiang, Y.; and Corey, M.\n\n\n \n\n\n\n arXiv preprint arXiv:2509.05829. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"AugmentingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{pyo2025augmenting,\n  title = {Augmenting Human-Centered Racial Covenant Detection and Georeferencing with Plug-and-Play NLP Pipelines},\n  author = {Pyo, Jiyoon and Jiao, Yuankun and Chiang, Yao-Yi and Corey, Michael},\n  journal = {arXiv preprint arXiv:2509.05829},\n  year = {2025},\n  doi = {10.48550/arXiv.2509.05829},\n  url = {https://arxiv.org/abs/2509.05829}\n}\n\n
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\n \n\n \n \n \n \n \n LIGHT: Multi-modal Text Linking on Historical Maps.\n \n \n \n\n\n \n Lin, Y.; Olson, R.; Wu, J.; Chiang, Y.; and Weinman, J.\n\n\n \n\n\n\n In Yin, X.; Karatzas, D.; and Lopresti, D., editor(s), Document Analysis and Recognition – ICDAR 2025, pages 60–77, Cham, 2025. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{10.1007/978-3-032-04617-8_4,\n  title = {LIGHT: Multi-modal Text Linking on Historical Maps},\n  author = {Lin, Yijun and Olson, Rhett and Wu, Junhan and Chiang, Yao-Yi and Weinman, Jerod},\n  editor = {Yin, Xu-Cheng and Karatzas, Dimosthenis and Lopresti, Daniel},\n  booktitle = {Document Analysis and Recognition -- ICDAR 2025},\n  year = {2025},\n  publisher = {Springer Nature Switzerland},\n  address = {Cham},\n  pages = {60--77},\n  isbn = {978-3-032-04617-8},\n  abstract = {Text on historical maps provides valuable information for studies in history, economics, geography, and other related fields. Unlike structured or semi-structured documents, text on maps varies significantly in orientation, reading order, shape, and placement. Many modern methods can detect and transcribe text regions, but they struggle to effectively ``link'' the recognized text fragments, e.g., determining a multi-word place name. Existing layout analysis methods model word relationships to improve text understanding in structured documents, but they primarily rely on linguistic features and neglect geometric information, which is essential for handling map text. To address these challenges, we propose LIGHT, a novel multi-modal approach that integrates linguistic, image, and geometric features for linking text on historical maps. In particular, LIGHT includes a geometry-aware embedding module that encodes the polygonal coordinates of text regions to capture polygon shapes and their relative spatial positions on an image. LIGHT unifies this geometric information with the visual and linguistic token embeddings from LayoutLMv3, a pretrained layout analysis model. LIGHT uses the cross-modal information to predict the reading-order successor of each text instance directly with a bi-directional learning strategy that enhances sequence robustness. Experimental results show that LIGHT outperforms existing methods on the ICDAR 2024/2025 MapText Competition data, demonstrating the effectiveness of multi-modal learning for historical map text linking.}\n}\n\n
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\n Text on historical maps provides valuable information for studies in history, economics, geography, and other related fields. Unlike structured or semi-structured documents, text on maps varies significantly in orientation, reading order, shape, and placement. Many modern methods can detect and transcribe text regions, but they struggle to effectively ``link'' the recognized text fragments, e.g., determining a multi-word place name. Existing layout analysis methods model word relationships to improve text understanding in structured documents, but they primarily rely on linguistic features and neglect geometric information, which is essential for handling map text. To address these challenges, we propose LIGHT, a novel multi-modal approach that integrates linguistic, image, and geometric features for linking text on historical maps. In particular, LIGHT includes a geometry-aware embedding module that encodes the polygonal coordinates of text regions to capture polygon shapes and their relative spatial positions on an image. LIGHT unifies this geometric information with the visual and linguistic token embeddings from LayoutLMv3, a pretrained layout analysis model. LIGHT uses the cross-modal information to predict the reading-order successor of each text instance directly with a bi-directional learning strategy that enhances sequence robustness. Experimental results show that LIGHT outperforms existing methods on the ICDAR 2024/2025 MapText Competition data, demonstrating the effectiveness of multi-modal learning for historical map text linking.\n
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\n \n\n \n \n \n \n \n LDTR: Linear Object Detection Transformer for Accurate Graph Generation by Learning the N-Hop Connectivity Information.\n \n \n \n\n\n \n Duan, W.; Chiang, Y.; and Knoblock, C. A.\n\n\n \n\n\n\n In Yin, X.; Karatzas, D.; and Lopresti, D., editor(s), Document Analysis and Recognition – ICDAR 2025, pages 40–59, Cham, 2025. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{10.1007/978-3-032-04617-8_3,\n  title = {LDTR: Linear Object Detection Transformer for Accurate Graph Generation by Learning the N-Hop Connectivity Information},\n  author = {Duan, Weiwei and Chiang, Yao-Yi and Knoblock, Craig A.},\n  editor = {Yin, Xu-Cheng and Karatzas, Dimosthenis and Lopresti, Daniel},\n  booktitle = {Document Analysis and Recognition -- ICDAR 2025},\n  year = {2025},\n  publisher = {Springer Nature Switzerland},\n  address = {Cham},\n  pages = {40--59},\n  isbn = {978-3-032-04617-8},\n  abstract = {Historical maps contain valuable, detailed survey data often unavailable elsewhere. Automatically extracting linear objects, such as fault lines, from scanned historical maps benefits diverse application areas, such as mining resource prediction. However, existing models encounter challenges in capturing adequate image context and spatial context. Insufficient image context leads to false detections by failing to distinguish desired linear objects from others with similar appearances. Meanwhile, insufficient spatial context hampers the accurate delineation of elongated, slender-shaped linear objects. This paper introduces the Linear Object Detection TRansformer (LDTR), which directly generates accurate vector graphs for linear objects from scanned map images. LDTR leverages multi-scale deformable attention to capture representative image context, reducing false detections. Furthermore, LDTR's innovative N-hop connectivity component explicitly encourages interactions among nodes within an N-hop neighborhood, enabling the model to learn sufficient spatial context for generating graphs with accurate connectivity. Experiments show that LDTR improves detection precision by 6{\\%} and enhances line connectivity by 20{\\%} over state-of-the-art baselines.}\n}\n\n
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\n Historical maps contain valuable, detailed survey data often unavailable elsewhere. Automatically extracting linear objects, such as fault lines, from scanned historical maps benefits diverse application areas, such as mining resource prediction. However, existing models encounter challenges in capturing adequate image context and spatial context. Insufficient image context leads to false detections by failing to distinguish desired linear objects from others with similar appearances. Meanwhile, insufficient spatial context hampers the accurate delineation of elongated, slender-shaped linear objects. This paper introduces the Linear Object Detection TRansformer (LDTR), which directly generates accurate vector graphs for linear objects from scanned map images. LDTR leverages multi-scale deformable attention to capture representative image context, reducing false detections. Furthermore, LDTR's innovative N-hop connectivity component explicitly encourages interactions among nodes within an N-hop neighborhood, enabling the model to learn sufficient spatial context for generating graphs with accurate connectivity. Experiments show that LDTR improves detection precision by 6% and enhances line connectivity by 20% over state-of-the-art baselines.\n
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\n \n\n \n \n \n \n \n Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data.\n \n \n \n\n\n \n Knoblock, C. A.; Vu, B.; Shbita, B.; Chiang, Y.; Krishna, P. P.; Lin, X.; Muric, G.; Pyo, J.; Trejo-Sheu, A.; and Ye, M.\n\n\n \n\n\n\n In Garijo, D.; Kirrane, S.; Salatino, A.; Shimizu, C.; Acosta, M.; Nuzzolese, A. G.; Ferrada, S.; Soulard, T.; Kozaki, K.; Takeda, H.; and Gentile, A. L., editor(s), The Semantic Web – ISWC 2025, pages 451–471, Cham, 2025. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{10.1007/978-3-032-09530-5_26,\n  title = {Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data},\n  author = {Knoblock, Craig A. and Vu, Binh and Shbita, Basel and Chiang, Yao-Yi and Krishna, Pothula Punith and Lin, Xiao and Muric, Goran and Pyo, Jiyoon and Trejo-Sheu, Adriana and Ye, Meng},\n  editor = {Garijo, Daniel and Kirrane, Sabrina and Salatino, Angelo and Shimizu, Cogan and Acosta, Maribel and Nuzzolese, Andrea Giovanni and Ferrada, Sebasti{\\'a}n and Soulard, Thibaut and Kozaki, Kouji and Takeda, Hideaki and Gentile, Anna Lisa},\n  booktitle = {The Semantic Web -- ISWC 2025},\n  year = {2025},\n  publisher = {Springer Nature Switzerland},\n  address = {Cham},\n  pages = {451--471},\n  isbn = {978-3-032-09530-5},\n  abstract = {Locating new sources of critical minerals begins with understanding where these minerals have been found in the past. However, historical data about mineral occurrences is often locked in disparate, unstructured, and inconsistent formats, ranging from government databases to mining reports and journal articles. To address this challenge, we have developed a set of scalable technologies that extract, normalize, and semantically integrate information from these sources into a unified knowledge graph. Our approach combines ontology-driven modeling, large-language models for information extraction and classification, and tools for linking and validating data across sources. The result is a semantically enriched, queryable knowledge graph that supports reproducible analysis, expert validation, and geoscientific applications such as deposit classification and prospectivity modeling. Through this work, we have successfully integrated information from hundreds of thousands of records across multiple historical sources to build one of the world's largest repositories of structured data on critical minerals.}\n}\n\n
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\n Locating new sources of critical minerals begins with understanding where these minerals have been found in the past. However, historical data about mineral occurrences is often locked in disparate, unstructured, and inconsistent formats, ranging from government databases to mining reports and journal articles. To address this challenge, we have developed a set of scalable technologies that extract, normalize, and semantically integrate information from these sources into a unified knowledge graph. Our approach combines ontology-driven modeling, large-language models for information extraction and classification, and tools for linking and validating data across sources. The result is a semantically enriched, queryable knowledge graph that supports reproducible analysis, expert validation, and geoscientific applications such as deposit classification and prospectivity modeling. Through this work, we have successfully integrated information from hundreds of thousands of records across multiple historical sources to build one of the world's largest repositories of structured data on critical minerals.\n
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\n \n\n \n \n \n \n \n AI-Supported Smart Search for Multi-Period Historical Maps Using the mapKurator Place Name Index.\n \n \n \n\n\n \n Pai, P.; Lu, H.; Su, W.; Liao, H.; Chan, T.; Lin, Y.; and Chiang, Y.\n\n\n \n\n\n\n Journal of Map & Geography Libraries,1-23. 2025.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{pai2025supported,\n  title = {AI-Supported Smart Search for Multi-Period Historical Maps Using the mapKurator Place Name Index},\n  author = {Pai, Pi-Ling and Lu, Hsiang-Hsi and Su, Wen-Rong and Liao, Hsiung-Ming and Chan, Ta-Chien and Lin, Yijun and Chiang, Yao-Yi},\n  journal = {Journal of Map & Geography Libraries},\n  year = {2025},\n  pages = {1-23},\n  doi = {10.1080/15420353.2025.2580239}\n}\n\n
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\n \n\n \n \n \n \n \n \n Detecting Legend Items on Historical Maps Using GPT-4o with In-Context Learning.\n \n \n \n \n\n\n \n Kirsanova, S.; Duan, W.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 4th ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, of GeoSearch '25, pages 35–38, New York, NY, USA, 2025. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"DetectingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764920.3770590,\n  title = {Detecting Legend Items on Historical Maps Using GPT-4o with In-Context Learning},\n  author = {Kirsanova, Sofia and Duan, Weiwei and Chiang, Yao-Yi},\n  year = {2025},\n  isbn = {9798400721830},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3764920.3770590},\n  doi = {10.1145/3764920.3770590},\n  booktitle = {Proceedings of the 4th ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data},\n  pages = {35–38},\n  numpages = {4},\n  keywords = {Map Digitization, Geospatial information retrieval, Map Layout Analysis, In-Context Learning},\n  series = {GeoSearch '25},\n  abstract = {Historical map legends are critical for interpreting cartographic symbols. However, their inconsistent layouts and unstructured formats make automatic extraction challenging. Prior work focuses primarily on segmentation or general optical character recognition (OCR), with few methods effectively matching legend symbols to their corresponding descriptions in a structured manner. We present a method that combines LayoutLMv3 for layout detection with GPT-4o using in-context learning to detect and link legend items and their descriptions via bounding-box predictions. Our experiments show that GPT-4 with structured JSON prompts outperforms the baseline, achieving 88\\% F-1 and 85\\% IoU, and reveal how prompt design, example counts, and layout alignment affect performance. This approach supports scalable, layout-aware legend parsing and improves the indexing and searchability of historical maps across various visual styles.}\n}\n\n
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\n Historical map legends are critical for interpreting cartographic symbols. However, their inconsistent layouts and unstructured formats make automatic extraction challenging. Prior work focuses primarily on segmentation or general optical character recognition (OCR), with few methods effectively matching legend symbols to their corresponding descriptions in a structured manner. We present a method that combines LayoutLMv3 for layout detection with GPT-4o using in-context learning to detect and link legend items and their descriptions via bounding-box predictions. Our experiments show that GPT-4 with structured JSON prompts outperforms the baseline, achieving 88% F-1 and 85% IoU, and reveal how prompt design, example counts, and layout alignment affect performance. This approach supports scalable, layout-aware legend parsing and improves the indexing and searchability of historical maps across various visual styles.\n
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\n \n\n \n \n \n \n \n \n BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data.\n \n \n \n \n\n\n \n Xie, J.; Kim, J.; Chiang, Y.; Zhao, L.; and Shafique, K.\n\n\n \n\n\n\n In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection, of GeoAnomalies '25, pages 56–59, New York, NY, USA, 2025. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"BeSTAD:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764914.3770888,\n  title = {BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data},\n  author = {Xie, Junyi and Kim, Jina and Chiang, Yao-Yi and Zhao, Lingyi and Shafique, Khurram},\n  year = {2025},\n  isbn = {9798400722608},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3764914.3770888},\n  doi = {10.1145/3764914.3770888},\n  booktitle = {Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection},\n  pages = {56–59},\n  numpages = {4},\n  keywords = {trajectory anomaly detection, mobility behavioral understanding, unsupervised learning},\n  series = {GeoAnomalies '25},\n  abstract = {Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within datasets encompassing large populations remains a significant challenge. In this paper, we present BeSTAD (Behavior-aware Spatio-Temporal Anomaly Detection for Human Mobility Data), an unsupervised framework that captures individualized behavioral signatures across large populations and uncovers fine-grained anomalies by jointly modeling spatial context and temporal dynamics. BeSTAD learns semantically enriched mobility representations that integrate location meaning and temporal patterns, enabling the detection of subtle deviations in individual movement behavior. BeSTAD further employs a behavior-cluster-aware modeling mechanism that builds personalized behavioral profiles from normal activity and identifies anomalies through cross-period behavioral comparison with consistent semantic alignment. Building on prior work in mobility behavior clustering, this approach enables not only the detection of behavioral shifts and deviations from established routines but also the identification of individuals exhibiting such changes within large-scale mobility datasets. By learning individual behaviors directly from unlabeled data, BeSTAD advances anomaly detection toward personalized and interpretable mobility analysis.}\n}\n\n
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\n Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within datasets encompassing large populations remains a significant challenge. In this paper, we present BeSTAD (Behavior-aware Spatio-Temporal Anomaly Detection for Human Mobility Data), an unsupervised framework that captures individualized behavioral signatures across large populations and uncovers fine-grained anomalies by jointly modeling spatial context and temporal dynamics. BeSTAD learns semantically enriched mobility representations that integrate location meaning and temporal patterns, enabling the detection of subtle deviations in individual movement behavior. BeSTAD further employs a behavior-cluster-aware modeling mechanism that builds personalized behavioral profiles from normal activity and identifies anomalies through cross-period behavioral comparison with consistent semantic alignment. Building on prior work in mobility behavior clustering, this approach enables not only the detection of behavioral shifts and deviations from established routines but also the identification of individuals exhibiting such changes within large-scale mobility datasets. By learning individual behaviors directly from unlabeled data, BeSTAD advances anomaly detection toward personalized and interpretable mobility analysis.\n
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\n \n\n \n \n \n \n \n \n CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health.\n \n \n \n \n\n\n \n Namgung, M.; Chiang, Y.; and Omitaomu, O. A.\n\n\n \n\n\n\n In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Urban-AI, of UrbanAI '25, pages 27–36, New York, NY, USA, 2025. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"CareWELL:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764926.3771947,\n  title = {CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health},\n  author = {Namgung, Min and Chiang, Yao-Yi and Omitaomu, Olufemi A.},\n  year = {2025},\n  isbn = {9798400721892},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3764926.3771947},\n  doi = {10.1145/3764926.3771947},\n  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Urban-AI},\n  pages = {27–36},\n  numpages = {10},\n  keywords = {Multimodal Learning, Region Representation Learning, Large Language Models},\n  series = {UrbanAI '25},\n  abstract = {Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.}\n}\n\n
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\n Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.\n
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\n \n\n \n \n \n \n \n \n Region Context from Unifying Points, Lines, and Polygons.\n \n \n \n \n\n\n \n Kim, J.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Urban-AI, of UrbanAI '25, pages 94–95, New York, NY, USA, 2025. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"RegionPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764926.3771941,\n  title = {Region Context from Unifying Points, Lines, and Polygons},\n  author = {Kim, Jina and Chiang, Yao-Yi},\n  year = {2025},\n  isbn = {9798400721892},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3764926.3771941},\n  doi = {10.1145/3764926.3771941},\n  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Urban-AI},\n  pages = {94–95},\n  numpages = {2},\n  keywords = {urban foundation models, region contextualization, spatial semantics},\n  series = {UrbanAI '25},\n  abstract = {We envision a new generation of urban foundation models that unify semantic, spatial, and topological relationships within and across point, line, and polygon features. Existing approaches typically encode different feature types separately and fuse them only at later stages, missing opportunities for truly integrated contextualization. To illustrate our vision, we present RegionContext, a framework that generates contextual region embeddings capturing both fine-grained spatial structures and higher-order semantics. Finally, we highlight key research directions for region contextualization in urban foundation models.}\n}\n\n
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\n We envision a new generation of urban foundation models that unify semantic, spatial, and topological relationships within and across point, line, and polygon features. Existing approaches typically encode different feature types separately and fuse them only at later stages, missing opportunities for truly integrated contextualization. To illustrate our vision, we present RegionContext, a framework that generates contextual region embeddings capturing both fine-grained spatial structures and higher-order semantics. Finally, we highlight key research directions for region contextualization in urban foundation models.\n
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\n \n\n \n \n \n \n \n \n Capturing Shared and Unique Information in Multimodal Region Representations for Urban Mobility Prediction.\n \n \n \n \n\n\n \n Namgung, M.; Lee, J.; Lin, Y.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Urban Mobility Foundation Models, of UMFM '25, pages 10–12, New York, NY, USA, 2025. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"CapturingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764925.3770911,\n  title = {Capturing Shared and Unique Information in Multimodal Region Representations for Urban Mobility Prediction},\n  author = {Namgung, Min and Lee, JangHyeon and Lin, Yijun and Chiang, Yao-Yi},\n  year = {2025},\n  isbn = {9798400721885},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3764925.3770911},\n  doi = {10.1145/3764925.3770911},\n  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Urban Mobility Foundation Models},\n  pages = {10–12},\n  numpages = {3},\n  keywords = {Multimodal Region Representation Learning, Mobility Demand Prediction},\n  series = {UMFM '25},\n  abstract = {Understanding urban mobility patterns requires integrating diverse geospatial data, including built environment, infrastructure, and demographics. Region Representation Learning (RRL) provides a promising way to incorporate such multimodal data, but existing contrastive learning approaches often overlook modality-specific information. We propose CooKIE, a multimodal RRL method that combines intra-view learning with pairwise inter-view learning to capture both shared and unique information efficiently. As a case study, we apply CooKIE to bike-sharing demand prediction at newly opened stations in New York City. Experimental results show that embeddings learned by CooKIE consistently outperform baseline method, demonstrating its effectiveness in modeling multimodal dependencies that drive urban mobility patterns.}\n}\n\n
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\n Understanding urban mobility patterns requires integrating diverse geospatial data, including built environment, infrastructure, and demographics. Region Representation Learning (RRL) provides a promising way to incorporate such multimodal data, but existing contrastive learning approaches often overlook modality-specific information. We propose CooKIE, a multimodal RRL method that combines intra-view learning with pairwise inter-view learning to capture both shared and unique information efficiently. As a case study, we apply CooKIE to bike-sharing demand prediction at newly opened stations in New York City. Experimental results show that embeddings learned by CooKIE consistently outperform baseline method, demonstrating its effectiveness in modeling multimodal dependencies that drive urban mobility patterns.\n
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\n \n\n \n \n \n \n \n \n WalkCLIP: Multimodal Learning for Urban Walkability Prediction.\n \n \n \n \n\n\n \n Xiang, S.; Lee, J.; Namgung, M.; and Chiang, Y.\n\n\n \n\n\n\n arXiv preprint arXiv:2511.21947. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"WalkCLIP:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{xiang2025walkclip,\n  title = {WalkCLIP: Multimodal Learning for Urban Walkability Prediction},\n  author = {Xiang, Shilong and Lee, JangHyeon and Namgung, Min and Chiang, Yao-Yi},\n  journal = {arXiv preprint arXiv:2511.21947},\n  year = {2025},\n  doi = {10.48550/arXiv.2511.21947},\n  url = {https://arxiv.org/abs/2511.21947}\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Automatic Search of Multiword Place Names on Historical Maps.\n \n \n \n \n\n\n \n Olson, R.; Kim, J.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, of GeoSearch '24, pages 9–12, New York, NY, USA, 2024. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3681769.3698577,\n  author = {Olson, Rhett and Kim, Jina and Chiang, Yao-Yi},\n  title = {Automatic Search of Multiword Place Names on Historical Maps},\n  year = {2024},\n  isbn = {9798400711480},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3681769.3698577},\n  doi = {10.1145/3681769.3698577},\n  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data},\n  pages = {9–12},\n  numpages = {4},\n  keywords = {cartography, historical maps, minimum spanning tree, toponym},\n  location = {Atlanta, GA, USA},\n  series = {GeoSearch '24}\n}\n\n
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\n \n\n \n \n \n \n \n \n MiTREE: Multi-input Transformer Ecoregion Encoder for Species Distribution Modelling.\n \n \n \n \n\n\n \n Chen, T.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, of GeoAI '24, pages 110–120, New York, NY, USA, 2024. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"MiTREE:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3687123.3698297,\n  author = {Chen, Theresa and Chiang, Yao-Yi},\n  title = {MiTREE: Multi-input Transformer Ecoregion Encoder for Species Distribution Modelling},\n  year = {2024},\n  isbn = {9798400711763},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3687123.3698297},\n  doi = {10.1145/3687123.3698297},\n  booktitle = {Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},\n  pages = {110–120},\n  numpages = {11},\n  keywords = {Multimodal machine learning, Spatial data, Species distribution modeling},\n  location = {Atlanta, GA, USA},\n  series = {GeoAI '24}\n}\n\n
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\n \n\n \n \n \n \n \n \n Enabling Semantic-Rich Location Search on Street View Imagery Using Multilingual POI Data.\n \n \n \n \n\n\n \n Jang, L.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, of GeoSearch '24, pages 29–35, New York, NY, USA, 2024. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"EnablingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{10.1145/3681769.3698583,\n  author = {Jang, Leeje and Chiang, Yao-Yi},\n  title = {Enabling Semantic-Rich Location Search on Street View Imagery Using Multilingual POI Data},\n  year = {2024},\n  isbn = {9798400711480},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3681769.3698583},\n  doi = {10.1145/3681769.3698583},\n  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data},\n  pages = {29–35},\n  numpages = {7},\n  location = {Atlanta, GA, USA},\n  series = {GeoSearch '24}\n}\n\n
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\n \n\n \n \n \n \n \n \n Leveraging Large Language Models for Generating Labeled Mineral Site Record Linkage Data.\n \n \n \n \n\n\n \n Pyo, J.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, of GeoAI '24, pages 86–98, New York, NY, USA, 2024. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"LeveragingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3687123.3698298,\n  author = {Pyo, Jiyoon and Chiang, Yao-Yi},\n  title = {Leveraging Large Language Models for Generating Labeled Mineral Site Record Linkage Data},\n  year = {2024},\n  isbn = {9798400711763},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3687123.3698298},\n  doi = {10.1145/3687123.3698298},\n  booktitle = {Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},\n  pages = {86–98},\n  numpages = {13},\n  keywords = {Spatial entity linkage, entity matching, geospatial data},\n  location = {Atlanta, GA, USA},\n  series = {GeoAI '24}\n}\n\n
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\n \n\n \n \n \n \n \n \n CrossBag: A Bag of Tricks for Cross-City Mobility Prediction.\n \n \n \n \n\n\n \n Lee, J.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge, of HuMob'24, pages 55–59, New York, NY, USA, 2024. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"CrossBag:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3681771.3699935,\n  author = {Lee, JangHyeon and Chiang, Yao-Yi},\n  title = {CrossBag: A Bag of Tricks for Cross-City Mobility Prediction},\n  year = {2024},\n  isbn = {9798400711503},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3681771.3699935},\n  doi = {10.1145/3681771.3699935},\n  abstract = {Access to large-scale human trajectory data has significantly advanced the understanding of human mobility (HuMob) behavior for urban planning. However, these data are often concentrated in major cities, leaving smaller or less-monitored areas with limited information, undermining the performance of data-hungry machine learning models for HuMob prediction. This imbalance poses a challenge for cross-city mobility prediction, as many existing models are designed for single-city settings. To address this, we present CrossBag, a set of simple yet effective techniques to boost cross-city prediction. These techniques include context-aware spatiotemporal embeddings, masking types, and a progressive knowledge transfer method to incrementally adapt the target model while preserving useful patterns from the source model for stable cross-city transfer. Additionally, we propose a test-time trajectory refinement method using top-K guided beam search to prevent predictors from getting stuck in repetitive location predictions. We validate CrossBag on the large-scale multi-city dataset from the HuMob Challenge 2024, achieving a top-10 placement out of over 100 participating teams.},\n  booktitle = {Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge},\n  pages = {55–59},\n  numpages = {5},\n  keywords = {Human mobility, Spatiotemporal, Transfer learning, Transformer},\n  location = {Atlanta, GA, USA},\n  series = {HuMob'24}\n}\n\n
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\n Access to large-scale human trajectory data has significantly advanced the understanding of human mobility (HuMob) behavior for urban planning. However, these data are often concentrated in major cities, leaving smaller or less-monitored areas with limited information, undermining the performance of data-hungry machine learning models for HuMob prediction. This imbalance poses a challenge for cross-city mobility prediction, as many existing models are designed for single-city settings. To address this, we present CrossBag, a set of simple yet effective techniques to boost cross-city prediction. These techniques include context-aware spatiotemporal embeddings, masking types, and a progressive knowledge transfer method to incrementally adapt the target model while preserving useful patterns from the source model for stable cross-city transfer. Additionally, we propose a test-time trajectory refinement method using top-K guided beam search to prevent predictors from getting stuck in repetitive location predictions. We validate CrossBag on the large-scale multi-city dataset from the HuMob Challenge 2024, achieving a top-10 placement out of over 100 participating teams.\n
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\n \n\n \n \n \n \n \n \n Hyper-Local Deformable Transformers for Text Spotting on Historical Maps.\n \n \n \n \n\n\n \n Lin, Y.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, of KDD '24, pages 5387–5397, New York, NY, USA, 2024. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"Hyper-LocalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3637528.3671589,\n    author = {Lin, Yijun and Chiang, Yao-Yi},\n    title = {Hyper-Local Deformable Transformers for Text Spotting on Historical Maps},\n    year = {2024},\n    isbn = {9798400704901},\n    publisher = {Association for Computing Machinery},\n    address = {New York, NY, USA},\n    url = {https://doi.org/10.1145/3637528.3671589},\n    doi = {10.1145/3637528.3671589},\n    booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},\n    pages = {5387–5397},\n    numpages = {11},\n    keywords = {historical maps, synthetic map data, text detection and recognition, text spotting},\n    location = {Barcelona, Spain},\n    series = {KDD '24}\n}\n\n
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\n  \n 2023\n \n \n (9)\n \n \n
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\n \n\n \n \n \n \n \n \n Modeling Spatially Varying Physical Dynamics for Spatiotemporal Predictive Learning.\n \n \n \n \n\n\n \n Lin, Y.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '23, New York, NY, USA, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ModelingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3589132.3625648,\n    author = {Lin, Yijun and Chiang, Yao-Yi},\n    title = {Modeling Spatially Varying Physical Dynamics for Spatiotemporal Predictive Learning},\n    year = {2023},\n    isbn = {9798400701689},\n    publisher = {Association for Computing Machinery},\n    address = {New York, NY, USA},\n    url = {https://doi.org/10.1145/3589132.3625648},\n    doi = {10.1145/3589132.3625648},\n    booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},\n    articleno = {98},\n    numpages = {11},\n    keywords = {physics-guided machine learning, spatiotemporal predictive learning, spatial AI},\n    location = {Hamburg, Germany},\n    series = {SIGSPATIAL '23}\n}\n\n
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\n \n\n \n \n \n \n \n \n GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding.\n \n \n \n \n\n\n \n Li, Z.; Zhou, W.; Chiang, Y.; and Chen, M.\n\n\n \n\n\n\n In Bouamor, H.; Pino, J.; and Bali, K., editor(s), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5227–5240, Singapore, December 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"GeoLM:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{li-etal-2023-geolm,\n    title = "{G}eo{LM}: Empowering Language Models for Geospatially Grounded Language Understanding",\n    author = "Li, Zekun  and\n      Zhou, Wenxuan  and\n      Chiang, Yao-Yi  and\n      Chen, Muhao",\n    editor = "Bouamor, Houda  and\n      Pino, Juan  and\n      Bali, Kalika",\n    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",\n    month = dec,\n    year = "2023",\n    address = "Singapore",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.emnlp-main.317",\n    doi = "10.18653/v1/2023.emnlp-main.317",\n    pages = "5227--5240",\n}\n
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\n \n\n \n \n \n \n \n GeoAI for the Digitization of Historical Maps.\n \n \n \n\n\n \n Chiang, Y.; Chen, M.; Duan, W.; Kim, J.; Knoblock, C. A; Leyk, S.; Li, Z.; Lin, Y.; Namgung, M.; Shbita, B.; and others\n\n\n \n\n\n\n In Handbook of Geospatial Artificial Intelligence, pages 217–247. CRC Press, 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 21 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@incollection{chiang2023geoai,\n  title={GeoAI for the Digitization of Historical Maps},\n  author={Chiang, Yao-Yi and Chen, Muhao and Duan, Weiwei and Kim, Jina and Knoblock, Craig A and Leyk, Stefan and Li, Zekun and Lin, Yijun and Namgung, Min and Shbita, Basel and others},\n  booktitle={Handbook of Geospatial Artificial Intelligence},\n  pages={217--247},\n  year={2023},\n  publisher={CRC Press}\n}\n
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\n \n\n \n \n \n \n \n \n The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps.\n \n \n \n \n\n\n \n Kim, J.; Li, Z.; Lin, Y.; Namgung, M.; Jang, L.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '23, New York, NY, USA, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3589132.3625579,\nauthor = {Kim, Jina and Li, Zekun and Lin, Yijun and Namgung, Min and Jang, Leeje and Chiang, Yao-Yi},\ntitle = {The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps},\nyear = {2023},\nisbn = {9798400701689},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3589132.3625579},\ndoi = {10.1145/3589132.3625579},\nbooktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},\narticleno = {35},\nnumpages = {4},\nkeywords = {linked data, text spotter, historical maps, automatic system},\nlocation = {, Hamburg, Germany, },\nseries = {SIGSPATIAL '23}\n}\n
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\n \n\n \n \n \n \n \n \n An Automatic Approach to Finding Geographic Name Changes on Historical Maps.\n \n \n \n \n\n\n \n Olson, R. M; Kim, J.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '23, New York, NY, USA, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3589132.3628368,\nauthor = {Olson, Rhett M and Kim, Jina and Chiang, Yao-Yi},\ntitle = {An Automatic Approach to Finding Geographic Name Changes on Historical Maps},\nyear = {2023},\nisbn = {9798400701689},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3589132.3628368},\ndoi = {10.1145/3589132.3628368},\nbooktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},\narticleno = {7},\nnumpages = {2},\nkeywords = {temporal analysis, toponym, cartography, historical maps},\nlocation = {<conf-loc>, <city>Hamburg</city>, <country>Germany</country>, </conf-loc>},\nseries = {SIGSPATIAL '23}\n}\n
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\n \n\n \n \n \n \n \n \n Towards Learning of Spatial Triad from Online Text.\n \n \n \n \n\n\n \n Kim, J.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '23, New York, NY, USA, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3589132.3628372,\nauthor = {Kim, Jina and Chiang, Yao-Yi},\ntitle = {Towards Learning of Spatial Triad from Online Text},\nyear = {2023},\nisbn = {9798400701689},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3589132.3628372},\ndoi = {10.1145/3589132.3628372},\nbooktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},\narticleno = {11},\nnumpages = {2},\nkeywords = {representation learning, online text, spatial triad},\nlocation = {<conf-loc>, <city>Hamburg</city>, <country>Germany</country>, </conf-loc>},\nseries = {SIGSPATIAL '23}\n}\n
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\n \n\n \n \n \n \n \n Water, Sanitation, and Hygiene (WaSH) insecurity in unhoused communities of Los Angeles, California.\n \n \n \n\n\n \n Avelar Portillo, L. J.; Kayser, G. L; Ko, C.; Vasquez, A.; Gonzalez, J.; Avelar, D. J.; Alvarenga, N.; Franklin, M.; and Chiang, Y.\n\n\n \n\n\n\n International Journal for Equity in Health, 22(1): 1–19. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{avelar2023water,\n  title={Water, Sanitation, and Hygiene (WaSH) insecurity in unhoused communities of Los Angeles, California},\n  author={Avelar Portillo, Lourdes Johanna and Kayser, Georgia L and Ko, Charlene and Vasquez, Angelica and Gonzalez, Jimena and Avelar, Diego Jose and Alvarenga, Nayib and Franklin, Meredith and Chiang, Yao-Yi},\n  journal={International Journal for Equity in Health},\n  volume={22},\n  number={1},\n  pages={1--19},\n  year={2023},\n  publisher={BioMed Central}\n}\n
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\n \n\n \n \n \n \n \n Optimal and efficient planning of charging stations for electric vehicles in urban areas: formulation, complexity and solutions.\n \n \n \n\n\n \n Zhang, Y.; Hua, Y.; Kang, A.; He, J.; Jia, M.; and Chiang, Y.\n\n\n \n\n\n\n Expert Systems with Applications, 230: 120442. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{zhang2023optimal,\n  title={Optimal and efficient planning of charging stations for electric vehicles in urban areas: formulation, complexity and solutions},\n  author={Zhang, Ying and Hua, Yunpeng and Kang, Ao and He, Jiyuan and Jia, Meng and Chiang, Yao-Yi},\n  journal={Expert Systems with Applications},\n  volume={230},\n  pages={120442},\n  year={2023},\n  publisher={Elsevier}\n}\n
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\n \n\n \n \n \n \n \n The Best Protection Is Attack: Fooling Scene Text Recognition with Minimal Pixels.\n \n \n \n\n\n \n Xu, Y.; Dai, P.; Li, Z.; Wang, H.; and Cao, X.\n\n\n \n\n\n\n IEEE Transactions on Information Forensics and Security. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{xu2023best,\n  title={The Best Protection Is Attack: Fooling Scene Text Recognition with Minimal Pixels},\n  author={Xu, Yikun and Dai, Pengwen and Li, Zekun and Wang, Hongjun and Cao, Xiaochun},\n  journal={IEEE Transactions on Information Forensics and Security},\n  year={2023},\n  publisher={IEEE}\n}\n\n\n
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\n  \n 2022\n \n \n (9)\n \n \n
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\n \n\n \n \n \n \n \n A Constraint-based Routing and Charging Methodology for Battery Electric Vehicles with Deep Reinforcement Learning.\n \n \n \n\n\n \n Zhang, Y.; Li, M.; Chen, Y.; Chiang, Y.; and Hua, Y.\n\n\n \n\n\n\n IEEE Transactions on Smart Grid,1-1. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{9924526,  \n  author={Zhang, Ying and Li, Muyang and Chen, Yuanchang and Chiang, Yao-Yi and Hua, Yunpeng},  \n  journal={IEEE Transactions on Smart Grid},   \n  title={A Constraint-based Routing and Charging Methodology for Battery Electric Vehicles with Deep Reinforcement Learning},   \n  year={2022},\n  pages={1-1},  \n  doi={10.1109/TSG.2022.3214680}}\n\n
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\n \n\n \n \n \n \n \n Clustering Human Mobility with Multiple Spaces.\n \n \n \n\n\n \n Hu, H.; Lin, H.; and Chiang, Y.\n\n\n \n\n\n\n In 2022 IEEE International Conference on Big Data (Big Data), pages 575–584, 2022. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{hu2022clustering,\n  title={Clustering Human Mobility with Multiple Spaces},\n  author={Hu, Haoji and Lin, Haowen and Chiang, Yao-Yi},\n  booktitle={2022 IEEE International Conference on Big Data (Big Data)},\n  pages={575--584},\n  year={2022},\n  organization={IEEE}\n}\n
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\n \n\n \n \n \n \n \n A Semi-Supervised Learning Approach for Abnormal Event Prediction on Large Network Operation Time-Series Data.\n \n \n \n\n\n \n Lin, Y.; and Chiang, Y.\n\n\n \n\n\n\n In 2022 IEEE International Conference on Big Data (Big Data), pages 1024–1033, 2022. IEEE\n \n\n\n\n
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@inproceedings{lin2022semi,\n  title={A Semi-Supervised Learning Approach for Abnormal Event Prediction on Large Network Operation Time-Series Data},\n  author={Lin, Yijun and Chiang, Yao-Yi},\n  booktitle={2022 IEEE International Conference on Big Data (Big Data)},\n  pages={1024--1033},\n  year={2022},\n  organization={IEEE}\n}\n
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\n \n\n \n \n \n \n \n SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation.\n \n \n \n\n\n \n Li, Z.; Kim, J.; Chiang, Y.; and Chen, M.\n\n\n \n\n\n\n arXiv preprint arXiv:2210.12213. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{li2022spabert,\n  title={SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation},\n  author={Li, Zekun and Kim, Jina and Chiang, Yao-Yi and Chen, Muhao},\n  journal={arXiv preprint arXiv:2210.12213},\n  year={2022}\n}\n
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\n \n\n \n \n \n \n \n Cell-by-Cell Line-of-Sight Probability Models Based on Real-World Base Station Deployment.\n \n \n \n\n\n \n Modad, B. A. A.; Yu, X.; Song, H. J.; Chiang, Y.; and Molisch, A. F\n\n\n \n\n\n\n In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 4782–4787, 2022. IEEE\n \n\n\n\n
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@inproceedings{modad2022cell,\n  title={Cell-by-Cell Line-of-Sight Probability Models Based on Real-World Base Station Deployment},\n  author={Modad, Bassel Abou Ali and Yu, Xin and Song, Hae Jin and Chiang, Yao-Yi and Molisch, Andreas F},\n  booktitle={GLOBECOM 2022-2022 IEEE Global Communications Conference},\n  pages={4782--4787},\n  year={2022},\n  organization={IEEE}\n}\n
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\n \n\n \n \n \n \n \n Incorporating spatial context for post-OCR in map images.\n \n \n \n\n\n \n Namgung, M.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 14–17, 2022. \n \n\n\n\n
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\n 
@inproceedings{namgung2022incorporating,\n  title={Incorporating spatial context for post-OCR in map images},\n  author={Namgung, Min and Chiang, Yao-Yi},\n  booktitle={Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},\n  pages={14--17},\n  year={2022}\n}\n\n
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\n \n\n \n \n \n \n \n ACE: Anchor-free corner evolution for real-time arbitrarily-oriented object detection.\n \n \n \n\n\n \n Dai, P.; Yao, S.; Li, Z.; Zhang, S.; and Cao, X.\n\n\n \n\n\n\n IEEE Transactions on Image Processing, 31: 4076–4089. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{dai2022ace,\n  title={ACE: Anchor-free corner evolution for real-time arbitrarily-oriented object detection},\n  author={Dai, Pengwen and Yao, Siyuan and Li, Zekun and Zhang, Sanyi and Cao, Xiaochun},\n  journal={IEEE Transactions on Image Processing},\n  volume={31},\n  pages={4076--4089},\n  year={2022},\n  publisher={IEEE}\n}\n\n
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\n \n\n \n \n \n \n \n \n Building Spatio-Temporal Knowledge Graphs from Vectorized Topographic Historical Maps.\n \n \n \n \n\n\n \n Shbita, B.; Knoblock, C. A; Duan, W.; Chiang, Y.; Uhl, J. H; and Leyk, S.\n\n\n \n\n\n\n Semantic Web, (Preprint): 1–23. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"BuildingLink\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 12 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{shbita2022building,\n  title={Building Spatio-Temporal Knowledge Graphs from Vectorized Topographic Historical Maps},\n  author={Shbita, Basel and Knoblock, Craig A and Duan, Weiwei and Chiang, Yao-Yi and Uhl, Johannes H and Leyk, Stefan},\n  journal={Semantic Web},\n  number={Preprint},\n  pages={1--23},\n  year={2022},\n  publisher={IOS Press},\n  urlLink={https://content.iospress.com/articles/semantic-web/sw222918}\n}\n\n
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\n \n\n \n \n \n \n \n \n Traffic volume prediction for scenic spots based on multi-source and heterogeneous data.\n \n \n \n \n\n\n \n Gao, Y.; Chiang, Y.; Zhang, X.; and Zhang, M.\n\n\n \n\n\n\n Transactions in GIS, 26(6): 2415-2439. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"TrafficPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{https://doi.org/10.1111/tgis.12975,\nauthor = {Gao, Yuan and Chiang, Yao-Yi and Zhang, Xiaoxi and Zhang, Min},\ntitle = {Traffic volume prediction for scenic spots based on multi-source and heterogeneous data},\njournal = {Transactions in GIS},\nvolume = {26},\nnumber = {6},\npages = {2415-2439},\ndoi = {https://doi.org/10.1111/tgis.12975},\nurl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12975},\neprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/tgis.12975},\nabstract = {Abstract Traffic prediction for scenic spots is an important topic in modeling an urban traffic system. Existing traffic prediction approaches typically use raw traffic data and road networks without considering the physical environment and human–environment interaction. This article presents a novel traffic prediction model that considers: (1) the topological structure of the city road network; (2) the popularity and accessibility of each scenic spot in the city; and (3) the traffic volumes of nearby scenic spots. The proposed model first learns a series of traffic dependency graphs by the Multi-graph Convolutional Network using multiple data sources describing historical traffic volumes, scenic spots popularity, land function, location, and accessibility. The graph nodes represent the scenic spots, and the links between them represent their traffic dependency, considering all traffic and geographic features. Then the proposed model uses the Gated Recurrent Unit (GRU) to capture the temporal dependency between multiple fused graphs for traffic volume prediction. The experiments show that the proposed model (M-GCNGRU) can effectively exploit and integrate geographic data with historical traffic data for traffic volume prediction, outperforming several classical and state-of-the-art methods.},\nyear = {2022}\n}\n\n\n
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\n Abstract Traffic prediction for scenic spots is an important topic in modeling an urban traffic system. Existing traffic prediction approaches typically use raw traffic data and road networks without considering the physical environment and human–environment interaction. This article presents a novel traffic prediction model that considers: (1) the topological structure of the city road network; (2) the popularity and accessibility of each scenic spot in the city; and (3) the traffic volumes of nearby scenic spots. The proposed model first learns a series of traffic dependency graphs by the Multi-graph Convolutional Network using multiple data sources describing historical traffic volumes, scenic spots popularity, land function, location, and accessibility. The graph nodes represent the scenic spots, and the links between them represent their traffic dependency, considering all traffic and geographic features. Then the proposed model uses the Gated Recurrent Unit (GRU) to capture the temporal dependency between multiple fused graphs for traffic volume prediction. The experiments show that the proposed model (M-GCNGRU) can effectively exploit and integrate geographic data with historical traffic data for traffic volume prediction, outperforming several classical and state-of-the-art methods.\n
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\n  \n 2021\n \n \n (10)\n \n \n
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\n \n\n \n \n \n \n \n Chartocr: Data extraction from charts images via a deep hybrid framework.\n \n \n \n\n\n \n Luo, J.; Li, Z.; Wang, J.; and Lin, C.\n\n\n \n\n\n\n In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 1917–1925, 2021. \n \n\n\n\n
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@inproceedings{luo2021chartocr,\n  title={Chartocr: Data extraction from charts images via a deep hybrid framework},\n  author={Luo, Junyu and Li, Zekun and Wang, Jinpeng and Lin, Chin-Yew},\n  booktitle={Proceedings of the IEEE/CVF winter conference on applications of computer vision},\n  pages={1917--1925},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Synthetic map generation to provide unlimited training data for historical map text detection.\n \n \n \n\n\n \n Li, Z.; Guan, R.; Yu, Q.; Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pages 17–26, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{li2021synthetic,\n  title={Synthetic map generation to provide unlimited training data for historical map text detection},\n  author={Li, Zekun and Guan, Runyu and Yu, Qianmu and Chiang, Yao-Yi and Knoblock, Craig A},\n  booktitle={Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},\n  pages={17--26},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n \n Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making.\n \n \n \n \n\n\n \n Gil, Y.; Garijo, D.; Khider, D.; Knoblock, C. A; Ratnakar, V.; Osorio, M.; Vargas, H.; Pham, M.; Pujara, J.; Shbita, B.; Vu, B.; Chiang, Y.; Feldman, D.; Lin, Y.; Song, H.; Kumar, V.; Khandelwal, A.; Steinbach, M.; Tayal, K.; Xu, S.; Pierce, S. A; Pearson, L.; Hardesty-Lewis, D.; Deelman, E.; Silva, R. F. D.; Mayani, R.; Kemanian, A. R; Shi, Y.; Leonard, L.; Peckham, S.; Stoica, M.; Cobourn, K.; Zhang, Z.; Duffy, C.; and Shu, L.\n\n\n \n\n\n\n ACM Trans. Interact. Intell. Syst., 11(2): 1–49. July 2021.\n \n\n\n\n
\n\n\n\n \n \n \"ArtificialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 25 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Gil2021-va,\n  title     = "{Artificial Intelligence for Modeling Complex Systems: Taming the\n               Complexity of Expert Models to Improve Decision Making}",\n  author    = "Gil, Yolanda and Garijo, Daniel and Khider, Deborah and\n               Knoblock, Craig A and Ratnakar, Varun and Osorio, Maximiliano\n               and Vargas, Hern{\\'a}n and Pham, Minh and Pujara, Jay and\n               Shbita, Basel and Vu, Binh and Chiang, Yao-Yi and Feldman, Dan\n               and Lin, Yijun and Song, Hayley and Kumar, Vipin and Khandelwal,\n               Ankush and Steinbach, Michael and Tayal, Kshitij and Xu,\n               Shaoming and Pierce, Suzanne A and Pearson, Lissa and\n               Hardesty-Lewis, Daniel and Deelman, Ewa and Silva, Rafael\n               Ferreira Da and Mayani, Rajiv and Kemanian, Armen R and Shi,\n               Yuning and Leonard, Lorne and Peckham, Scott and Stoica, Maria\n               and Cobourn, Kelly and Zhang, Zeya and Duffy, Christopher and\n               Shu, Lele",\n  journal   = "ACM Trans. Interact. Intell. Syst.",\n  publisher = "Association for Computing Machinery",\n  volume    =  11,\n  number    =  2,\n  pages     = "1--49",\n  month     =  jul,\n  year      =  2021,\n  url       = "https://doi.org/10.1145/3453172",\n  file      = "yaoyichi.github.io/papers-all//Gil-et-al.-2021-Artificial-Intelligence-for-Modeling-Complex-Systems-Taming-the-Complexity-of-Expert-Models-to-Improve-Decision-Making.pdf",\n  address   = "New York, NY, USA",\n  issn      = "2160-6455",\n  doi       = "10.1145/3453172"\n}\n\n
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\n \n\n \n \n \n \n \n Measuring Closest Water, Sanitation, and Hygiene Facilities in Unhoused Communities of Los Angeles.\n \n \n \n\n\n \n Avelar Portillo, L. J.; Park, L.; Ko, C.; Vasquez, A.; Franklin, M.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 30th International Cartographic Conference (ICC'21), 2021. \n \n\n\n\n
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@INPROCEEDINGS{Avelar21-icc,\n  title     = "{Measuring Closest Water, Sanitation, and Hygiene Facilities in Unhoused Communities of Los Angeles}",\n  booktitle = "{{Proceedings of the 30th International Cartographic Conference\n               (ICC'21)}}",\n  author    = "Avelar Portillo, L. J. and Park, L. and Ko, C. and Vasquez, A. and Franklin, M. and Chiang, Y.-Y. ",\n  year      =  2021\n}\n\n
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\n \n\n \n \n \n \n \n Reimagining Measures of Spatial Access to Health Care in Low- And Middle-Income Countries: Using Road Network Analysis to Validate Self-Reported Perceptions of Geospatial Barriers.\n \n \n \n\n\n \n Park, L.; Birdwell, A.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 30th International Cartographic Conference (ICC'21), 2021. \n \n\n\n\n
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@INPROCEEDINGS{Park21-icc,\n  title     = "{Reimagining Measures of Spatial Access to Health Care in Low- And Middle-Income Countries: Using Road Network Analysis to Validate Self-Reported Perceptions of Geospatial Barriers}",\n  booktitle = "{{Proceedings of the 30th International Cartographic Conference\n               (ICC'21)}}",\n  author    = "Park, L. and Birdwell, A. and Chiang, Y.-Y. ",\n  year      =  2021\n}\n\n
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\n \n\n \n \n \n \n \n Impacts of COVID-19 on Water, Sanitation, and Hygiene (WaSH) Access in Skid Row, Los Angeles.\n \n \n \n\n\n \n Avelar Portillo, L. J.; Chiang, Y.; Franklin, M.; Ko, C.; and Vasquez, A.\n\n\n \n\n\n\n In American Association of Geographers Annual Meeting, 2021. \n \n\n\n\n
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@INPROCEEDINGS{Avelar21-aag,\n  title     = "{Impacts of COVID-19 on Water, Sanitation, and Hygiene (WaSH) Access in Skid Row, Los Angeles}",\n  booktitle = "{American Association of Geographers Annual Meeting}",\n  author    = "Avelar Portillo, L. J. and Chiang, Y.-Y. and Franklin, M. and Ko, C. and Vasquez, A. ",\n  year      =  2021\n}\n\n
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\n \n\n \n \n \n \n \n \n BiS4EV: A fast routing algorithm considering charging stations and preferences for electric vehicles.\n \n \n \n \n\n\n \n Zhang, Y.; Wu, B.; Chiang, Y.; Zhang, X.; Chen, Y.; Li, M.; and Li, F.\n\n\n \n\n\n\n Engineering applications of artificial intelligence, 104: 104378. September 2021.\n \n\n\n\n
\n\n\n\n \n \n \"BiS4EV:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Zhang2021-ju,\n  title   = "{BiS4EV: A fast routing algorithm considering charging stations and\n             preferences for electric vehicles}",\n  author  = "Zhang, Ying and Wu, Bin and Chiang, Yao-Yi and Zhang, Xin and\n             Chen, Yuanchang and Li, Muyang and Li, Fanyu",\n  journal = "Engineering applications of artificial intelligence",\n  volume  =  104,\n  pages   = "104378",\n  month   =  sep,\n  year    =  2021,\n  url     = "https://www.sciencedirect.com/science/article/pii/S0952197621002268",\n  issn    = "0952-1976",\n  doi     = "10.1016/j.engappai.2021.104378"\n}\n\n
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\n \n\n \n \n \n \n \n \n W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets.\n \n \n \n \n\n\n \n Li, K.; Deng, H.; Morrison, J.; Habre, R.; Franklin, M.; Chiang, Y.; Sward, K.; Gilliland, F. D.; Ambite, J. L.; and Eckel, S. P.\n\n\n \n\n\n\n Sensors, 21(17). 2021.\n \n\n\n\n
\n\n\n\n \n \n \"W-TSS:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{s21175801,\nAUTHOR = {Li, Kenan and Deng, Huiyu and Morrison, John and Habre, Rima and Franklin, Meredith and Chiang, Yao-Yi and Sward, Katherine and Gilliland, Frank D. and Ambite, José Luis and Eckel, Sandrah P.},\nTITLE = {W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets},\nJOURNAL = {Sensors},\nVOLUME = {21},\nYEAR = {2021},\nNUMBER = {17},\nARTICLE-NUMBER = {5801},\nURL = {https://www.mdpi.com/1424-8220/21/17/5801},\nPubMedID = {34502692},\nISSN = {1424-8220},\nABSTRACT = {Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.},\nDOI = {10.3390/s21175801}\n}\n\n\n\n
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\n Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.\n
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\n \n\n \n \n \n \n \n \n VAMBC: A Variational Approach for Mobility Behavior Clustering.\n \n \n \n \n\n\n \n Yue, M.; Chiang, Y.; and Shahabi, C.\n\n\n \n\n\n\n In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, pages 453–469, 2021. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \n \"VAMBC:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Yue2021-aa,\n  title     = "{VAMBC: A Variational Approach for Mobility Behavior Clustering}",\n  booktitle = "{Machine Learning and Knowledge Discovery in Databases. Applied\n               Data Science Track}",\n  author    = "Yue, Mingxuan and Chiang, Yao-Yi and Shahabi, Cyrus",\n  publisher = "Springer International Publishing",\n  pages     = "453--469",\n  year      =  2021,\n  url       = "http://dx.doi.org/10.1007/978-3-030-86514-6_28",\n  doi       = "10.1007/978-3-030-86514-6_28"\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents.\n \n \n \n \n\n\n \n Uhl, J. H; Leyk, S.; Li, Z.; Duan, W.; Shbita, B.; Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n Remote Sensing, 13(18): 3672. September 2021.\n \n\n\n\n
\n\n\n\n \n \n \"CombiningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Uhl2021-dj,\n  title     = "{Combining Remote-Sensing-Derived Data and Historical Maps for\n               Long-Term Back-Casting of Urban Extents}",\n  author    = "Uhl, Johannes H and Leyk, Stefan and Li, Zekun and Duan, Weiwei\n               and Shbita, Basel and Chiang, Yao-Yi and Knoblock, Craig A",\n  journal   = "Remote Sensing",\n  publisher = "Multidisciplinary Digital Publishing Institute",\n  volume    =  13,\n  number    =  18,\n  pages     = "3672",\n  month     =  sep,\n  year      =  2021,\n  url       = "https://www.mdpi.com/2072-4292/13/18/3672",\n  file      = "yaoyichi.github.io/papers-all//Uhl-et-al.-2021-Combining-Remote-Sensing-Derived-Data-and-Historical-Maps-for-Long-Term-Back-Casting-of-Urban-Extents.pdf",\n  language  = "en",\n  doi       = "10.3390/rs13183672"\n}\n
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\n  \n 2020\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n \n Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction.\n \n \n \n \n\n\n \n Lin, Y; Chiang, Y.; Franklin, M; Eckel, P S; and Ambite, J L\n\n\n \n\n\n\n In Proceedings of IEEE International Conference on Data Mining (ICDM), 2020. \n \n\n\n\n
\n\n\n\n \n \n \"Building-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 28 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Lin2020-kk,\n  title     = "{Building Autocorrelation-Aware Representations for Fine-Scale\n               Spatiotemporal Prediction}",\n  booktitle = "{Proceedings of IEEE International Conference on Data Mining\n               (ICDM)}",\n  author    = "Lin, Y and Chiang, Y-Y and Franklin, M and Eckel, P S and\n               Ambite, J L",\n  year      =  2020,\n  url-file      = "papers/Lin-et-al.-2020-Building-Autocorrelation-Aware-Representations-for-Fine-Scale-Spatiotemporal-Prediction.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning.\n \n \n \n \n\n\n \n Duan, W.; Chiang, Y.; Leyk, S.; Uhl, J. H; and Knoblock, C. A\n\n\n \n\n\n\n International journal of geographical information science: IJGIS, 34(4): 824–849. April 2020.\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n \n \"Automatic-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 31 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Duan2020-wl,\n  title     = "{Automatic alignment of contemporary vector data and\n               georeferenced historical maps using reinforcement learning}",\n  author    = "Duan, Weiwei and Chiang, Yao-Yi and Leyk, Stefan and Uhl,\n               Johannes H and Knoblock, Craig A",\n  journal   = "International journal of geographical information science: IJGIS",\n  publisher = "Taylor \\& Francis",\n  volume    =  34,\n  number    =  4,\n  pages     = "824--849",\n  month     =  apr,\n  year      =  2020,\n  url       = "https://doi.org/10.1080/13658816.2019.1698742",\n  url-file      = "papers/Duan-et-al.-2020-Automatic-alignment-of-contemporary-vector-data-and-georeferenced-historical-maps-using-reinforcement-learning.pdf",\n  issn      = "1365-8816",\n  doi       = "10.1080/13658816.2019.1698742"\n}\n\n
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\n \n\n \n \n \n \n \n \n Efficient Deployment of Electric Vehicle Charging Infrastructure: Simultaneous Optimization of Charging Station Placement and Charging Pile Assignment.\n \n \n \n \n\n\n \n Zhang, Y; Wang, Y; Li, F; Wu, B; Chiang, Y Y; and Zhang, X\n\n\n \n\n\n\n IEEE Transactions on Intelligent Transportation Systems,1–6. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"EfficientPaper\n  \n \n \n \"Efficient-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Zhang2020-rb,\n  title   = "{Efficient Deployment of Electric Vehicle Charging Infrastructure:\n             Simultaneous Optimization of Charging Station Placement and\n             Charging Pile Assignment}",\n  author  = "Zhang, Y and Wang, Y and Li, F and Wu, B and Chiang, Y Y and\n             Zhang, X",\n  journal = "IEEE Transactions on Intelligent Transportation Systems",\n  pages   = "1--6",\n  year    =  2020,\n  url     = "http://dx.doi.org/10.1109/TITS.2020.2990694",\n  url-file    = "papers/Zhang-et-al.-2020-Efficient-Deployment-of-Electric-Vehicle-Charging-In...-timization-of-Charging-Station-Placement-and-Charging-Pile-Assignment.pdf",\n  issn    = "1558-0016",\n  doi     = "10.1109/TITS.2020.2990694"\n}\n\n
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\n \n\n \n \n \n \n \n \n Using Historical Maps in Scientific Studies: Applications, Challenges, and Best Practices.\n \n \n \n \n\n\n \n Chiang, Y.; Duan, W.; Leyk, S.; Uhl, J. H; and Knoblock, C. A\n\n\n \n\n\n\n of SpringerBriefs in GeographySpringer International Publishing, 2020.\n \n\n\n\n
\n\n\n\n \n \n \"UsingPaper\n  \n \n \n \"Using-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 39 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@BOOK{Chiang2020-vm,\n  title     = "{Using Historical Maps in Scientific Studies: Applications,\n               Challenges, and Best Practices}",\n  author    = "Chiang, Yao-Yi and Duan, Weiwei and Leyk, Stefan and Uhl,\n               Johannes H and Knoblock, Craig A",\n  publisher = "Springer International Publishing",\n  series    = "SpringerBriefs in Geography",\n  year      =  2020,\n  url       = "https://link.springer.com/book/10.1007/978-3-319-66908-3",\n  url-file      = "papers/Chiang-et-al.-2020-Using-Historical-Maps-in-Scientific-Studies-Applications,-Challenges,-and-Best-Practices.pdf",\n  isbn      = "9783319669076",\n  doi       = "10.1007/978-3-319-66908-3"\n}\n\n
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\n \n\n \n \n \n \n \n \n Design, Development, Testing, and Deployment of GIS Applications.\n \n \n \n \n\n\n \n Chiang, Y.; and Lin, Y.\n\n\n \n\n\n\n In Wilson, J. P, editor(s), The Geographic Information Science & Technology Body of Knowledge (4th Quarter 2020 Edition). The University Consortium for Geographic Information Science (UCGIS), 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Design,Paper\n  \n \n \n \"Design,-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INCOLLECTION{Chiang2020-zz,\n  title     = "{Design, Development, Testing, and Deployment of GIS Applications}",\n  booktitle = "{The Geographic Information Science \\& Technology Body of\n               Knowledge (4th Quarter 2020 Edition)}",\n  author    = "Chiang, Yao-Yi and Lin, Yijun",\n  editor    = "Wilson, John P",\n  publisher = "The University Consortium for Geographic Information Science\n               (UCGIS)",\n  year      =  2020,\n  url       = "http://dx.doi.org/10.22224/gistbok/2020.4.2",\n  url-file      = "papers/Chiang-and-Lin-2020-Design,-Development,-Testing,-and-Deployment-of-GIS-Applications.pdf",\n  doi       = "10.22224/gistbok/2020.4.2"\n}\n\n
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\n \n\n \n \n \n \n \n \n An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images.\n \n \n \n \n\n\n \n Li, Z; Chiang, Y Y; Tavakkol, S; Shbita, B; Uhl, J H; and others\n\n\n \n\n\n\n Proceedings of ACM Knowledge Discovery and Data Mining Conference (KDD). 2020.\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n \n \"An-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 23 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Li2020-my,\n  title     = "{An Automatic Approach for Generating Rich, Linked Geo-Metadata\n               from Historical Map Images}",\n  author    = "Li, Z and Chiang, Y Y and Tavakkol, S and Shbita, B and Uhl, J H\n               and {others}",\n  journal   = "Proceedings of ACM Knowledge Discovery and Data Mining\n               Conference (KDD)",\n  publisher = "dl.acm.org",\n  year      =  2020,\n  url       = "https://dl.acm.org/doi/abs/10.1145/3394486.3403381",\n  url-file      = "papers/Li-et-al.-2020-An-Automatic-Approach-for-Generating-Rich,-Linked-Geo-Metadata-from-Historical-Map-Images.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Building Linked Spatio-Temporal Data from Vectorized Historical Maps.\n \n \n \n \n\n\n \n Shbita, B.; Knoblock, C. A; Duan, W.; Chiang, Y.; Uhl, J. H; and Leyk, S.\n\n\n \n\n\n\n In The Semantic Web, pages 409–426, 2020. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \n \"BuildingPaper\n  \n \n \n \"Building-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 39 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Shbita2020-tl,\n  title     = "{Building Linked Spatio-Temporal Data from Vectorized Historical\n               Maps}",\n  booktitle = "{The Semantic Web}",\n  author    = "Shbita, Basel and Knoblock, Craig A and Duan, Weiwei and Chiang,\n               Yao-Yi and Uhl, Johannes H and Leyk, Stefan",\n  publisher = "Springer International Publishing",\n  pages     = "409--426",\n  year      =  2020,\n  url       = "http://dx.doi.org/10.1007/978-3-030-49461-2_24",\n  url-file      = "papers/Shbita-et-al.-2020-Building-Linked-Spatio-Temporal-Data-from-Vectorized-Historical-Maps.pdf",\n  doi       = "10.1007/978-3-030-49461-2\\_24"\n}\n\n
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\n \n\n \n \n \n \n \n \n A Review of Air Quality Modeling.\n \n \n \n \n\n\n \n Karroum, K.; Lin, Y.; Chiang, Y.; Ben Maissa, Y.; El Haziti, M.; Sokolov, A.; and Delbarre, H.\n\n\n \n\n\n\n MAPAN. March 2020.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 12 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Karroum2020-ku,\n  title   = "{A Review of Air Quality Modeling}",\n  author  = "Karroum, Khaoula and Lin, Yijun and Chiang, Yao-Yi and Ben Maissa,\n             Yann and El Haziti, Mohamed and Sokolov, Anton and Delbarre,\n             Herv{\\'e}",\n  journal = "MAPAN",\n  month   =  mar,\n  year    =  2020,\n  url     = "https://doi.org/10.1007/s12647-020-00371-8",\n  url-file    = "papers/Karroum-et-al.-2020-A-Review-of-Air-Quality-Modeling.pdf",\n  issn    = "0974-9853",\n  doi     = "10.1007/s12647-020-00371-8"\n}\n\n
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\n  \n 2019\n \n \n (13)\n \n \n
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\n \n\n \n \n \n \n \n \n Automatic intersection extraction and building arrangement with StarCraft II maps.\n \n \n \n \n\n\n \n Cheng, Y.; and Chiang, Y.\n\n\n \n\n\n\n SIGSPATIAL Special, 10(3): 4–5. January 2019.\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n \n \"Automatic-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Cheng2019-mi,\n  title     = "{Automatic intersection extraction and building arrangement with\n               StarCraft II maps}",\n  author    = "Cheng, Yuanbin and Chiang, Yao-Yi",\n  journal   = "SIGSPATIAL Special",\n  publisher = "Association for Computing Machinery",\n  volume    =  10,\n  number    =  3,\n  pages     = "4--5",\n  month     =  jan,\n  year      =  2019,\n  url       = "https://doi.org/10.1145/3307599.3307602",\n  url-file      = "papers/Cheng-and-Chiang-2019-Automatic-intersection-extraction-and-building-arrangement-with-StarCraft-II-maps.pdf",\n  address   = "New York, NY, USA",\n  doi       = "10.1145/3307599.3307602"\n}\n\n
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\n \n\n \n \n \n \n \n \n An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models.\n \n \n \n \n\n\n \n Garijo, D.; Khider, D.; Ratnakar, V.; Gil, Y.; Deelman, E.; da Silva, R. F.; Knoblock, C.; Chiang, Y.; Pham, M.; Pujara, J.; Vu, B.; Feldman, D.; Mayani, R.; Cobourn, K.; Duffy, C.; Kemanian, A.; Shu, L.; Kumar, V.; Khandelwal, A.; Tayal, K.; Peckham, S.; Stoica, M.; Dabrowski, A.; Hardesty-Lewis, D.; and Pierce, S.\n\n\n \n\n\n\n In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion, of IUI '19, pages 111–112, New York, NY, USA, March 2019. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n \n \"An-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Garijo2019-yw,\n  title     = "{An intelligent interface for integrating climate, hydrology,\n               agriculture, and socioeconomic models}",\n  booktitle = "{Proceedings of the 24th International Conference on Intelligent\n               User Interfaces: Companion}",\n  author    = "Garijo, Daniel and Khider, Deborah and Ratnakar, Varun and Gil,\n               Yolanda and Deelman, Ewa and da Silva, Rafael Ferreira and\n               Knoblock, Craig and Chiang, Yao-Yi and Pham, Minh and Pujara,\n               Jay and Vu, Binh and Feldman, Dan and Mayani, Rajiv and Cobourn,\n               Kelly and Duffy, Chris and Kemanian, Armen and Shu, Lele and\n               Kumar, Vipin and Khandelwal, Ankush and Tayal, Kshitij and\n               Peckham, Scott and Stoica, Maria and Dabrowski, Anna and\n               Hardesty-Lewis, Daniel and Pierce, Suzanne",\n  publisher = "Association for Computing Machinery",\n  pages     = "111--112",\n  series    = "IUI '19",\n  month     =  mar,\n  year      =  2019,\n  url       = "https://doi.org/10.1145/3308557.3308711",\n  url-file      = "papers/Garijo-et-al.-2019-An-intelligent-interface-for-integrating-climate,-hydrology,-agriculture,-and-socioeconomic-models.pdf",\n  address   = "New York, NY, USA",\n  location  = "Marina del Ray, California",\n  isbn      = "9781450366731",\n  doi       = "10.1145/3308557.3308711"\n}\n\n
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\n \n\n \n \n \n \n \n \n A VLOS Compliance Solution to Ground/Aerial Parcel Delivery Problem.\n \n \n \n \n\n\n \n Zhang, J; Shen, T; Wang, W; Jiang, X; Ku, W; Sun, M; and Chiang, Y Y\n\n\n \n\n\n\n In Proceedings of the 2019 20th IEEE International Conference on Mobile Data Management (MDM), pages 201–209, June 2019. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Zhang2019-cl,\n  title     = "{A VLOS Compliance Solution to Ground/Aerial Parcel Delivery\n               Problem}",\n  booktitle = "{Proceedings of the 2019 20th IEEE International Conference on\n               Mobile Data Management (MDM)}",\n  author    = "Zhang, J and Shen, T and Wang, W and Jiang, X and Ku, W and Sun,\n               M and Chiang, Y Y",\n  pages     = "201--209",\n  month     =  jun,\n  year      =  2019,\n  url       = "http://dx.doi.org/10.1109/MDM.2019.00-56",\n  url-file      = "papers/Zhang-et-al.-2019-A-VLOS-Compliance-Solution-to-Ground-Aerial-Parcel-Delivery-Problem.pdf",\n  issn      = "2375-0324",\n  doi       = "10.1109/MDM.2019.00-56"\n}\n\n
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\n \n\n \n \n \n \n \n \n Creating a FAIR Data Catalog to Support Scientific Modeling.\n \n \n \n \n\n\n \n Shbita, B.; Vu, B.; Feldman, D.; Pham, M.; Rajendran, A; Knoblock, C A; Pujara, J; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the Workshop on Advanced Knowledge Technologies for Science in a FAIR World (AKTS) 2019, September 2019. \n \n\n\n\n
\n\n\n\n \n \n \"CreatingPaper\n  \n \n \n \"Creating-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Shbita2019-lv,\n  title     = "{Creating a FAIR Data Catalog to Support Scientific Modeling}",\n  booktitle = "{Proceedings of the Workshop on Advanced Knowledge Technologies\n               for Science in a FAIR World (AKTS) 2019}",\n  author    = "Shbita, Basel and Vu, Binh and Feldman, Dan and Pham, Minh and\n               Rajendran, A and Knoblock, C A and Pujara, J and Chiang, Y-Y",\n  month     =  sep,\n  year      =  2019,\n  url       = "https://www.researchgate.net/publication/339670920_Creating_a_FAIR_Data_Catalog_to_Support_Scientific_Modeling",\n  url-file      = "papers/Shbita-et-al.-2019-Creating-a-FAIR-Data-Catalog-to-Support-Scientific-Modeling.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Personalized Recommendation Method of POI Based on Deep Neural Network.\n \n \n \n \n\n\n \n Gao, Y; Duan, Z; Shi, W; Feng, J; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), pages 1–6, October 2019. \n \n\n\n\n
\n\n\n\n \n \n \"PersonalizedPaper\n  \n \n \n \"Personalized-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Gao2019-fo,\n  title     = "{Personalized Recommendation Method of POI Based on Deep Neural\n               Network}",\n  booktitle = "{Proceedings of the 2019 6th International Conference on\n               Behavioral, Economic and Socio-Cultural Computing (BESC)}",\n  author    = "Gao, Y and Duan, Z and Shi, W and Feng, J and Chiang, Y-Y",\n  pages     = "1--6",\n  month     =  oct,\n  year      =  2019,\n  url       = "http://dx.doi.org/10.1109/BESC48373.2019.8963449",\n  url-file      = "papers/Gao-et-al.-2019-Personalized-Recommendation-Method-of-POI-Based-on-Deep-Neural-Network.pdf",\n  doi       = "10.1109/BESC48373.2019.8963449"\n}\n\n
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\n \n\n \n \n \n \n \n \n Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data.\n \n \n \n \n\n\n \n Li, K.; Habre, R.; Deng, H.; Urman, R.; Morrison, J.; Gilliland, F. D; Ambite, J. L.; Stripelis, D.; Chiang, Y.; Lin, Y.; Bui, A. A.; King, C.; Hosseini, A.; Van Vliet, E.; Sarrafzadeh, M.; and Eckel, S. P\n\n\n \n\n\n\n JMIR mHealth and uHealth, 7(2): e11201. February 2019.\n \n\n\n\n
\n\n\n\n \n \n \"ApplyingPaper\n  \n \n \n \"Applying-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Li2019-gr,\n  title    = "{Applying Multivariate Segmentation Methods to Human Activity\n              Recognition From Wearable Sensors' Data}",\n  author   = "Li, Kenan and Habre, Rima and Deng, Huiyu and Urman, Robert and\n              Morrison, John and Gilliland, Frank D and Ambite, Jos{\\'e} Luis\n              and Stripelis, Dimitris and Chiang, Yao-Yi and Lin, Yijun and\n              Bui, Alex At and King, Christine and Hosseini, Anahita and Van\n              Vliet, Eleanne and Sarrafzadeh, Majid and Eckel, Sandrah P",\n  journal  = "JMIR mHealth and uHealth",\n  volume   =  7,\n  number   =  2,\n  pages    = "e11201",\n  month    =  feb,\n  year     =  2019,\n  url      = "http://dx.doi.org/10.2196/11201",\n  url-file     = "papers/Li-et-al.-2019-Applying-Multivariate-Segmentation-Methods-to-Human-Activity-Recognition-From-Wearable-Sensors'-Data.pdf",\n  language = "en",\n  issn     = "2291-5222",\n  pmid     = "30730297",\n  doi      = "10.2196/11201",\n  pmc      = "PMC6386646"\n}\n\n
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\n \n\n \n \n \n \n \n \n A New Gabor Filter-Based Method for Automatic Recognition of Hatched Residential Areas.\n \n \n \n \n\n\n \n Wu, J; Wei, P; Yuan, X; Shu, Z; Chiang, Y Y; Fu, Z; and Deng, M\n\n\n \n\n\n\n IEEE Access, 7: 40649–40662. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@ARTICLE{Wu2019-qe,\n  title   = "{A New Gabor Filter-Based Method for Automatic Recognition of\n             Hatched Residential Areas}",\n  author  = "Wu, J and Wei, P and Yuan, X and Shu, Z and Chiang, Y Y and Fu, Z\n             and Deng, M",\n  journal = "IEEE Access",\n  volume  =  7,\n  pages   = "40649--40662",\n  year    =  2019,\n  url     = "http://dx.doi.org/10.1109/ACCESS.2019.2907114",\n  url-file    = "papers/Wu-et-al.-2019-A-New-Gabor-Filter-Based-Method-for-Automatic-Recognition-of-Hatched-Residential-Areas.pdf",\n  issn    = "2169-3536",\n  doi     = "10.1109/ACCESS.2019.2907114"\n}\n\n
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\n \n\n \n \n \n \n \n \n Extracting geographic features from the Internet: A geographic information mining framework.\n \n \n \n \n\n\n \n Zhang, Y.; Ma, Q.; Chiang, Y.; Knoblock, C.; Zhang, X.; Yang, P.; Gao, M.; and Hu, X.\n\n\n \n\n\n\n Knowledge-Based Systems, 174: 57–72. June 2019.\n \n\n\n\n
\n\n\n\n \n \n \"ExtractingPaper\n  \n \n \n \"Extracting-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Zhang2019-rj,\n  title   = "{Extracting geographic features from the Internet: A geographic\n             information mining framework}",\n  author  = "Zhang, Ying and Ma, Qunfei and Chiang, Yao-Yi and Knoblock, Craig\n             and Zhang, Xin and Yang, Puhai and Gao, Minghe and Hu, Xiang",\n  journal = "Knowledge-Based Systems",\n  volume  =  174,\n  pages   = "57--72",\n  month   =  jun,\n  year    =  2019,\n  url     = "http://www.sciencedirect.com/science/article/pii/S0950705119300929",\n  url-file    = "papers/Zhang-et-al.-2019-Extracting-geographic-features-from-the-Internet-A-geographic-information-mining-framework.pdf",\n  issn    = "0950-7051",\n  doi     = "10.1016/j.knosys.2019.02.031"\n}\n\n
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\n \n\n \n \n \n \n \n \n Building Explainable Predictive Analytics for Location-Dependent Time-Series Data.\n \n \n \n \n\n\n \n Chiang, Y Y; Lin, Y; Franklin, M; Eckel, S P; Ambite, J L; and Ku, W\n\n\n \n\n\n\n In Proceedings of the 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI), pages 202–209, December 2019. \n \n\n\n\n
\n\n\n\n \n \n \"BuildingPaper\n  \n \n \n \"Building-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@INPROCEEDINGS{Chiang2019-yx,\n  title     = "{Building Explainable Predictive Analytics for Location-Dependent\n               Time-Series Data}",\n  booktitle = "{Proceedings of the 2019 IEEE First International Conference on\n               Cognitive Machine Intelligence (CogMI)}",\n  author    = "Chiang, Y Y and Lin, Y and Franklin, M and Eckel, S P and\n               Ambite, J L and Ku, W",\n  pages     = "202--209",\n  month     =  dec,\n  year      =  2019,\n  url       = "http://dx.doi.org/10.1109/CogMI48466.2019.00037",\n  url-file      = "papers/Chiang-et-al.-2019-Building-Explainable-Predictive-Analytics-for-Location-Dependent-Time-Series-Data.pdf",\n  doi       = "10.1109/CogMI48466.2019.00037"\n}\n\n
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\n \n\n \n \n \n \n \n \n ADMSv2: A Modern Architecture for Transportation Data Management and Analysis.\n \n \n \n \n\n\n \n Anastasiou, C.; Lin, J.; He, C.; Chiang, Y.; and Shahabi, C.\n\n\n \n\n\n\n In Proceedings of the 2nd ACM SIGSPATIAL workshop ARIC'19, New York, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"ADMSv2:-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Anastasiou2019-gk,\n  title     = "{ADMSv2: A Modern Architecture for Transportation Data Management\n               and Analysis}",\n  booktitle = "{Proceedings of the 2nd ACM SIGSPATIAL workshop ARIC'19}",\n  author    = "Anastasiou, Chrysovalantis and Lin, Jianfa and He, Chaoyang and\n               Chiang, Yao-Yi and Shahabi, Cyrus",\n  year      =  2019,\n  url-file      = "papers/Anastasiou-et-al.-2019-ADMSv2-A-Modern-Architecture-for-Transportation-Data-Management-and-Analysis.pdf;papers/Anastasiou-et-al.-2019-ADMSv2-A-Modern-Architecture-for-Transportation-Data-Management-and-Analysis.pdf",\n  address   = "New York"\n}\n\n
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\n \n\n \n \n \n \n \n \n DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis.\n \n \n \n \n\n\n \n Yue, M; Li, Y; Yang, H; Ahuja, R; Chiang, Y; and Shahabi, C\n\n\n \n\n\n\n In Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), pages 988–997, December 2019. \n \n\n\n\n
\n\n\n\n \n \n \"DETECT:Paper\n  \n \n \n \"DETECT:-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Yue2019-hn,\n  title     = "{DETECT: Deep Trajectory Clustering for Mobility-Behavior\n               Analysis}",\n  booktitle = "{Proceedings of the 2019 IEEE International Conference on Big\n               Data (Big Data)}",\n  author    = "Yue, M and Li, Y and Yang, H and Ahuja, R and Chiang, Y and\n               Shahabi, C",\n  pages     = "988--997",\n  month     =  dec,\n  year      =  2019,\n  url       = "http://dx.doi.org/10.1109/BigData47090.2019.9006561",\n  url-file      = "papers/Yue-et-al.-2019-DETECT-Deep-Trajectory-Clustering-for-Mobility-Behavior-Analysis.pdf",\n  doi       = "10.1109/BigData47090.2019.9006561"\n}\n\n
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\n \n\n \n \n \n \n \n \n Kartta Labs: Unrendering Historical Maps.\n \n \n \n \n\n\n \n Tavakkol, S.; Chiang, Y.; Waters, T.; Han, F.; Prasad, K.; and Kiveris, R.\n\n\n \n\n\n\n In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, of GeoAI 2019, pages 48–51, New York, NY, USA, November 2019. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"KarttaPaper\n  \n \n \n \"Kartta-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Tavakkol2019-xs,\n  title     = "{Kartta Labs: Unrendering Historical Maps}",\n  booktitle = "{Proceedings of the 3rd ACM SIGSPATIAL International Workshop on\n               AI for Geographic Knowledge Discovery}",\n  author    = "Tavakkol, Sasan and Chiang, Yao-Yi and Waters, Tim and Han, Feng\n               and Prasad, Kisalaya and Kiveris, Raimondas",\n  publisher = "Association for Computing Machinery",\n  pages     = "48--51",\n  series    = "GeoAI 2019",\n  month     =  nov,\n  year      =  2019,\n  url       = "https://doi.org/10.1145/3356471.3365236",\n  url-file      = "papers/Tavakkol-et-al.-2019-Kartta-Labs-Unrendering-Historical-Maps.pdf",\n  address   = "New York, NY, USA",\n  location  = "Chicago, IL, USA",\n  isbn      = "9781450369572",\n  doi       = "10.1145/3356471.3365236"\n}\n\n
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\n \n\n \n \n \n \n \n \n MINT: An intelligent interface for understanding the impacts of climate change on hydrological, agricultural and economic systems.\n \n \n \n \n\n\n \n Khider, D; Gil, Y; Cobourn, K M; Deelman, E; Duffy, C; Ferreira da Silva, R; Kemanian, A; Knoblock, C; Kumar, V; Peckham, S D; Chiang, Y Y; Feldman, D; Garijo, D; Hardesty Lewis, D; Khandelwal, A; Mayani, R; Osorio, M; Pahm, M; Pierce, S A; Pujara, J; Ratnakar, V; Shu, L; Song, H J; Shbita, B; Stoica, M; Vu, B; and Pearson, L\n\n\n \n\n\n\n AGU Fall Meeting Abstracts, December 2019.\n \n\n\n\n
\n\n\n\n \n \n \"MINT:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@MISC{Khider2019-zm,\n  title        = "{MINT: An intelligent interface for understanding the impacts\n                  of climate change on hydrological, agricultural and economic\n                  systems}",\n  author       = "Khider, D and Gil, Y and Cobourn, K M and Deelman, E and\n                  Duffy, C and Ferreira da Silva, R and Kemanian, A and\n                  Knoblock, C and Kumar, V and Peckham, S D and Chiang, Y Y and\n                  Feldman, D and Garijo, D and Hardesty Lewis, D and\n                  Khandelwal, A and Mayani, R and Osorio, M and Pahm, M and\n                  Pierce, S A and Pujara, J and Ratnakar, V and Shu, L and\n                  Song, H J and Shbita, B and Stoica, M and Vu, B and Pearson,\n                  L",\n  month        =  dec,\n  year         =  2019,\n  url          = "https://ui.adsabs.harvard.edu/abs/2019AGUFMPA33C1108K",\n  howpublished = "AGU Fall Meeting Abstracts"\n}\n\n
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\n  \n 2018\n \n \n (15)\n \n \n
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\n \n\n \n \n \n \n \n \n Next Generation Framework for Imagery Recognition and Analysis.\n \n \n \n \n\n\n \n Chiang, Y Y; and Feldman, D\n\n\n \n\n\n\n The 1st workshop of the NSF project: SI2-S2I2 Conceptualization: Geospatial Software Institute (GSI), 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Next-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@MISC{Chiang2018-ls,\n  title        = "{Next Generation Framework for Imagery Recognition and\n                  Analysis}",\n  author       = "Chiang, Y Y and Feldman, D",\n  year         =  2018,\n  howpublished = "The 1st workshop of the NSF project: SI2-S2I2\n                  Conceptualization: Geospatial Software Institute (GSI)",\n  url-file         = "papers/Chiang-and-Feldman-2018-Next-Generation-Framework-for-Imagery-Recognition-and-Analysis.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n MAPINS: An Intra-City PM2.5 Modeling Web Application Using A Scalable Data Management and Analysis System Integrating Public Multi-Source Data.\n \n \n \n \n\n\n \n Yu, X; Cheng, Y; Lin, Y; Chiang, Y Y; Stripelis, D; and Ambite, J L\n\n\n \n\n\n\n In Proceedings of the AutoCarto, pages 135 – 145, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"MAPINS:-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@INPROCEEDINGS{Yu2018-tj,\n  title     = "{MAPINS: An Intra-City PM2.5 Modeling Web Application Using A\n               Scalable Data Management and Analysis System Integrating Public\n               Multi-Source Data}",\n  booktitle = "{Proceedings of the AutoCarto}",\n  author    = "Yu, X and Cheng, Y and Lin, Y and Chiang, Y Y and Stripelis, D\n               and Ambite, J L",\n  pages     = "135 -- 145",\n  year      =  2018,\n  url-file      = "papers/Yu-et-al.-2018-MAPINS-An-Intra-City-PM2.5-Modeling-Web-Application-U...-a-Management-and-Analysis-System-Integrating-Public-Multi-Source-Data.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Automatic generation of precisely delineated geographic features from georeferenced historical maps using deep learning.\n \n \n \n \n\n\n \n Duan, W.; Chiang, Y; Knoblock, C. A; Leyk, S.; and Uhl, J\n\n\n \n\n\n\n In Proceedings of the AutoCarto, pages 59–63, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"Automatic-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Duan2018-cx,\n  title     = "{Automatic generation of precisely delineated geographic features\n               from georeferenced historical maps using deep learning}",\n  booktitle = "{Proceedings of the AutoCarto}",\n  author    = "Duan, Weiwei and Chiang, Y and Knoblock, Crang A and Leyk,\n               Stefan and Uhl, J",\n  pages     = "59--63",\n  year      =  2018,\n  url-file      = "papers/Duan-et-al.-2018-Automatic-generation-of-precisely-delineated-geographic-features-from-georeferenced-historical-maps-using-deep-learning.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Exploring the Potential of Deep Learning for Settlement Symbol Extraction from Historical Map Documents.\n \n \n \n \n\n\n \n Uhl, J. H; Leyk, S.; Chiang, Y.; Duan, W.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the UCGIS/AutoCarto, pages 123 – 124, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"Exploring-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Uhl2018-cf,\n  title     = "{Exploring the Potential of Deep Learning for Settlement Symbol\n               Extraction from Historical Map Documents}",\n  booktitle = "{Proceedings of the UCGIS/AutoCarto}",\n  author    = "Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\n               Weiwei and Knoblock, Craig A",\n  pages     = "123 -- 124",\n  year      =  2018,\n  url-file      = "papers/Uhl-et-al.-2018-Exploring-the-Potential-of-Deep-Learning-for-Settlement-Symbol-Extraction-from-Historical-Map-Documents.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n MINT: Model Integration Through Knowledge-Powered Data and Process Composition.\n \n \n \n \n\n\n \n Gil, Y.; Cobourn, K.; Deelman, E.; Duffy, C.; da Silva, R. F.; Kemanian, A.; Knoblock, C.; Kumar, V.; Peckham, S.; Carvalho, L.; Chiang, Y.; Garijo, D.; Khider, D.; Khandelwal, A.; Pahm, M.; Pujara, J.; Ratnakar, V.; Stoica, M.; and Vu, B.\n\n\n \n\n\n\n In Proceedings of the 9th International Congress on Environmental Modelling and Software, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"MINT:-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 50 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Gil2018-hg,\n  title     = "{MINT: Model Integration Through Knowledge-Powered Data and\n               Process Composition}",\n  booktitle = "{Proceedings of the 9th International Congress on Environmental\n               Modelling and Software}",\n  author    = "Gil, Yolanda and Cobourn, Kelly and Deelman, Ewa and Duffy,\n               Chris and da Silva, Rafael Ferreira and Kemanian, Armen and\n               Knoblock, Craig and Kumar, Vipin and Peckham, Scott and\n               Carvalho, Lucas and Chiang, Yao-Yi and Garijo, Daniel and\n               Khider, Deborah and Khandelwal, Ankush and Pahm, Minh and\n               Pujara, Jay and Ratnakar, Varun and Stoica, Maria and Vu, Binh",\n  year      =  2018,\n  url-file      = "papers/Gil-et-al.-2018-MINT-Model-Integration-Through-Knowledge-Powered-Data-and-Process-Composition.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Building Linked Data from Historical Maps.\n \n \n \n \n\n\n \n Lin, C.; Su, H.; Knoblock, C. A; Chiang, Y.; Duan, W.; Leyk, S.; and Uhl, J. H\n\n\n \n\n\n\n In Proceedings of the SemSci 2018: Enabling Open Semantic Science, pages 59–67, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"BuildingPaper\n  \n \n \n \"Building-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Lin2018-ck,\n  title     = "{Building Linked Data from Historical Maps}",\n  booktitle = "{Proceedings of the SemSci 2018: Enabling Open Semantic Science}",\n  author    = "Lin, Chun and Su, Hang and Knoblock, Craig A and Chiang, Yao-Yi\n               and Duan, Weiwei and Leyk, Stefan and Uhl, Johannes H",\n  pages     = "59--67",\n  year      =  2018,\n  url       = "ftp://ceur-ws.org/pub/publications/CEUR-WS/Vol-2184.zip",\n  url-file      = "papers/Lin-et-al.-2018-Building-Linked-Data-from-Historical-Maps.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n An Uncertainty Aware Method for Geographic Data Conflation.\n \n \n \n \n\n\n \n Lin, H.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, of BigSpatial 2018, pages 20–27, New York, NY, USA, November 2018. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n \n \"An-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Lin2018-vk,\n  title     = "{An Uncertainty Aware Method for Geographic Data Conflation}",\n  booktitle = "{Proceedings of the 7th ACM SIGSPATIAL International Workshop on\n               Analytics for Big Geospatial Data}",\n  author    = "Lin, Haowen and Chiang, Yao-Yi",\n  publisher = "Association for Computing Machinery",\n  pages     = "20--27",\n  series    = "BigSpatial 2018",\n  month     =  nov,\n  year      =  2018,\n  url       = "https://doi.org/10.1145/3282834.3282842",\n  url-file      = "papers/Lin-and-Chiang-2018-An-Uncertainty-Aware-Method-for-Geographic-Data-Conflation.pdf",\n  address   = "New York, NY, USA",\n  location  = "Seattle, WA, USA",\n  isbn      = "9781450360418",\n  doi       = "10.1145/3282834.3282842"\n}\n\n
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\n \n\n \n \n \n \n \n \n Los angeles metro bus data analysis using GPS trajectory and schedule data (demo paper).\n \n \n \n \n\n\n \n Nguyen, K.; Yang, J.; Lin, Y.; Lin, J.; Chiang, Y.; and Shahabi, C.\n\n\n \n\n\n\n In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '18, pages 560–563, New York, NY, USA, November 2018. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"LosPaper\n  \n \n \n \"Los-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Nguyen2018-am,\n  title     = "{Los angeles metro bus data analysis using GPS trajectory and\n               schedule data (demo paper)}",\n  booktitle = "{Proceedings of the 26th ACM SIGSPATIAL International Conference\n               on Advances in Geographic Information Systems}",\n  author    = "Nguyen, Kien and Yang, Jingyun and Lin, Yijun and Lin, Jianfa\n               and Chiang, Yao-Yi and Shahabi, Cyrus",\n  publisher = "Association for Computing Machinery",\n  pages     = "560--563",\n  series    = "SIGSPATIAL '18",\n  month     =  nov,\n  year      =  2018,\n  url       = "https://doi.org/10.1145/3274895.3274911",\n  url-file      = "papers/Nguyen-et-al.-2018-Los-angeles-metro-bus-data-analysis-using-GPS-trajectory-and-schedule-data-(demo-paper).pdf",\n  address   = "New York, NY, USA",\n  location  = "Seattle, Washington",\n  isbn      = "9781450358897",\n  doi       = "10.1145/3274895.3274911"\n}\n\n
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\n \n\n \n \n \n \n \n \n SRC: a fully automatic geographic feature recognition system.\n \n \n \n \n\n\n \n Duan, W.; and Chiang, Y.\n\n\n \n\n\n\n SIGSPATIAL Special, 9(3): 6–7. January 2018.\n \n\n\n\n
\n\n\n\n \n \n \"SRC:Paper\n  \n \n \n \"SRC:-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Duan2018-rv,\n  title     = "{SRC: a fully automatic geographic feature recognition system}",\n  author    = "Duan, Weiwei and Chiang, Yao-Yi",\n  journal   = "SIGSPATIAL Special",\n  publisher = "Association for Computing Machinery",\n  volume    =  9,\n  number    =  3,\n  pages     = "6--7",\n  month     =  jan,\n  year      =  2018,\n  url       = "https://doi.org/10.1145/3178392.3178396",\n  url-file      = "papers/Duan-and-Chiang-2018-SRC-a-fully-automatic-geographic-feature-recognition-system.pdf",\n  address   = "New York, NY, USA",\n  doi       = "10.1145/3178392.3178396"\n}\n\n
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\n \n\n \n \n \n \n \n \n A Matching Algorithm Based on Voronoi Diagram for Multi-Scale Polygonal Residential Areas.\n \n \n \n \n\n\n \n Wu, J; Wan, Y; Chiang, Y Y; Fu, Z; and Deng, M\n\n\n \n\n\n\n IEEE Access, 6: 4904–4915. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Wu2018-nr,\n  title   = "{A Matching Algorithm Based on Voronoi Diagram for Multi-Scale\n             Polygonal Residential Areas}",\n  author  = "Wu, J and Wan, Y and Chiang, Y Y and Fu, Z and Deng, M",\n  journal = "IEEE Access",\n  volume  =  6,\n  pages   = "4904--4915",\n  year    =  2018,\n  url     = "http://dx.doi.org/10.1109/ACCESS.2018.2793302",\n  url-file    = "papers/Wu-et-al.-2018-A-Matching-Algorithm-Based-on-Voronoi-Diagram-for-Multi-Scale-Polygonal-Residential-Areas.pdf",\n  issn    = "2169-3536",\n  doi     = "10.1109/ACCESS.2018.2793302"\n}\n\n
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\n \n\n \n \n \n \n \n \n Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.\n \n \n \n \n\n\n \n VoPham, T.; Hart, J. E; Laden, F.; and Chiang, Y.\n\n\n \n\n\n\n Environmental health: a global access science source, 17(1): 40. April 2018.\n \n\n\n\n
\n\n\n\n \n \n \"EmergingPaper\n  \n \n \n \"Emerging-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{VoPham2018-zv,\n  title    = "{Emerging trends in geospatial artificial intelligence (geoAI):\n              potential applications for environmental epidemiology}",\n  author   = "VoPham, Trang and Hart, Jaime E and Laden, Francine and Chiang,\n              Yao-Yi",\n  journal  = "Environmental health: a global access science source",\n  volume   =  17,\n  number   =  1,\n  pages    = "40",\n  month    =  apr,\n  year     =  2018,\n  url      = "http://dx.doi.org/10.1186/s12940-018-0386-x",\n  url-file     = "papers/VoPham-et-al.-2018-Emerging-trends-in-geospatial-artificial-intelligence-(geoAI)-potential-applications-for-environmental-epidemiology.pdf",\n  language = "en",\n  issn     = "1476-069X",\n  pmid     = "29665858",\n  doi      = "10.1186/s12940-018-0386-x",\n  pmc      = "PMC5905121"\n}\n\n
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\n \n\n \n \n \n \n \n \n SRC: automatic extraction of phrase-level map labels from historical maps.\n \n \n \n \n\n\n \n Lin, H.; and Chiang, Y.\n\n\n \n\n\n\n SIGSPATIAL Special, 9(3): 14–15. January 2018.\n \n\n\n\n
\n\n\n\n \n \n \"SRC:Paper\n  \n \n \n \"SRC:-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Lin2018-wz,\n  title     = "{SRC: automatic extraction of phrase-level map labels from\n               historical maps}",\n  author    = "Lin, Haowen and Chiang, Yao-Yi",\n  journal   = "SIGSPATIAL Special",\n  publisher = "Association for Computing Machinery",\n  volume    =  9,\n  number    =  3,\n  pages     = "14--15",\n  month     =  jan,\n  year      =  2018,\n  url       = "https://doi.org/10.1145/3178392.3178400",\n  url-file      = "papers/Lin-and-Chiang-2018-SRC-automatic-extraction-of-phrase-level-map-labels-from-historical-maps.pdf",\n  address   = "New York, NY, USA",\n  doi       = "10.1145/3178392.3178400"\n}\n\n
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\n \n\n \n \n \n \n \n \n Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning.\n \n \n \n \n\n\n \n Lin, Y.; Mago, N.; Gao, Y.; Li, Y.; Chiang, Y.; Shahabi, C.; and Ambite, J. L.\n\n\n \n\n\n\n In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '18, pages 359–368, New York, NY, USA, November 2018. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\n  \n \n \n \"Exploiting-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Lin2018-xw,\n  title     = "{Exploiting spatiotemporal patterns for accurate air quality\n               forecasting using deep learning}",\n  booktitle = "{Proceedings of the 26th ACM SIGSPATIAL International Conference\n               on Advances in Geographic Information Systems}",\n  author    = "Lin, Yijun and Mago, Nikhit and Gao, Yu and Li, Yaguang and\n               Chiang, Yao-Yi and Shahabi, Cyrus and Ambite, Jos{\\'e} Luis",\n  publisher = "Association for Computing Machinery",\n  pages     = "359--368",\n  series    = "SIGSPATIAL '18",\n  month     =  nov,\n  year      =  2018,\n  url       = "https://doi.org/10.1145/3274895.3274907",\n  url-file      = "papers/Lin-et-al.-2018-Exploiting-spatiotemporal-patterns-for-accurate-air-quality-forecasting-using-deep-learning.pdf",\n  address   = "New York, NY, USA",\n  location  = "Seattle, Washington",\n  isbn      = "9781450358897",\n  doi       = "10.1145/3274895.3274907"\n}\n\n
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\n \n\n \n \n \n \n \n \n Map archive mining: visual-analytical approaches to explore large historical map collections.\n \n \n \n \n\n\n \n Uhl, J. H; Leyk, S.; Chiang, Y.; Duan, W.; and Knoblock, C. A\n\n\n \n\n\n\n ISPRS international journal of geo-information, 7(4): 148. April 2018.\n \n\n\n\n
\n\n\n\n \n \n \"MapPaper\n  \n \n \n \"Map-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@ARTICLE{Uhl2018-tj,\n  title     = "{Map archive mining: visual-analytical approaches to explore\n               large historical map collections}",\n  author    = "Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\n               Weiwei and Knoblock, Craig A",\n  journal   = "ISPRS international journal of geo-information",\n  publisher = "Multidisciplinary Digital Publishing Institute",\n  volume    =  7,\n  number    =  4,\n  pages     = "148",\n  month     =  apr,\n  year      =  2018,\n  url       = "http://dx.doi.org/10.3390/ijgi7040148",\n  url-file      = "papers/Uhl-et-al.-2018-Map-archive-mining-visual-analytical-approaches-to-explore-large-historical-map-collections.pdf",\n  language  = "en",\n  issn      = "2220-9964",\n  pmid      = "31061817",\n  doi       = "10.3390/ijgi7040148",\n  pmc       = "PMC6500493"\n}\n\n
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\n \n\n \n \n \n \n \n \n Spatialising uncertainty in image segmentation using weakly supervised convolutional neural networks: a case study from historical map processing.\n \n \n \n \n\n\n \n Uhl, J. H; Leyk, S.; Chiang, Y.; Duan, W.; and Knoblock, C. A\n\n\n \n\n\n\n IET Image Processing, 12(11): 2084–2091. July 2018.\n \n\n\n\n
\n\n\n\n \n \n \"SpatialisingPaper\n  \n \n \n \"Spatialising-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Uhl2018-ph,\n  title     = "{Spatialising uncertainty in image segmentation using weakly\n               supervised convolutional neural networks: a case study from\n               historical map processing}",\n  author    = "Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\n               Weiwei and Knoblock, Craig A",\n  journal   = "IET Image Processing",\n  publisher = "IET Digital Library",\n  volume    =  12,\n  number    =  11,\n  pages     = "2084--2091",\n  month     =  jul,\n  year      =  2018,\n  url       = "https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5484",\n  url-file      = "papers/Uhl-et-al.-2018-Spatialising-uncertainty-in-image-segmentation-using-w...-utional-neural-networks-a-case-study-from-historical-map-processing.pdf",\n  issn      = "1751-9667, 1751-9667",\n  doi       = "10.1049/iet-ipr.2018.5484"\n}\n\n
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\n  \n 2017\n \n \n (11)\n \n \n
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\n \n\n \n \n \n \n \n Implementing the Concept of Geographic Context for Efficient Recognition from Large-Scale Topographic Map Series.\n \n \n \n\n\n \n Leyk, S.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 28th International Cartographic Conference, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Leyk2017-zc,\n  title     = "{Implementing the Concept of Geographic Context for Efficient\n               Recognition from Large-Scale Topographic Map Series}",\n  booktitle = "{Proceedings of the 28th International Cartographic Conference}",\n  author    = "Leyk, Stefan and Chiang, Yao-Yi",\n  year      =  2017\n}\n\n
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\n \n\n \n \n \n \n \n Visual Knowledge Aggregation: From Static to Dynamic Information Systems in Library Contexts.\n \n \n \n\n\n \n Nanetti, A; Cattaneo, A; Cheong, S A; Chiang, Y Y; and Lin, C Y\n\n\n \n\n\n\n In Proceedings of the ICA Pre-Conference Workshop on Mapping Tools for Non-Mapping Experts: Incorporating Geospatial Visualization Tools in Libraries, 2017. \n \n\n\n\n
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@INPROCEEDINGS{Nanetti2017-tf,\n  title     = "{Visual Knowledge Aggregation: From Static to Dynamic Information\n               Systems in Library Contexts}",\n  booktitle = "{Proceedings of the ICA Pre-Conference Workshop on Mapping Tools\n               for Non-Mapping Experts: Incorporating Geospatial Visualization\n               Tools in Libraries}",\n  author    = "Nanetti, A and Cattaneo, A and Cheong, S A and Chiang, Y Y and\n               Lin, C Y",\n  year      =  2017\n}\n\n
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\n \n\n \n \n \n \n \n \n Automatic Learning of User Design Rationales from Examples.\n \n \n \n \n\n\n \n Chiang, Y Y; Jain, A; Bandyopadhyay, B; and Knoblock, A C\n\n\n \n\n\n\n In Proceedings of the Symposium on Solid and Physical Modeling (SPM), 2017. \n \n\n\n\n
\n\n\n\n \n \n \"Automatic-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2017-qn,\n  title     = "{Automatic Learning of User Design Rationales from Examples}",\n  booktitle = "{Proceedings of the Symposium on Solid and Physical Modeling\n               (SPM)}",\n  author    = "Chiang, Y Y and Jain, A and Bandyopadhyay, B and Knoblock, A C",\n  year      =  2017,\n  url-file      = "papers/Chiang-et-al.-2017-Automatic-Learning-of-User-Design-Rationales-from-Examples.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n Unlocking Maps for Discovery and Other Purpose.\n \n \n \n\n\n \n Holmes-Wong, D; and Chiang, Y Y\n\n\n \n\n\n\n Digital Library Federation (DLF) Forum, 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@MISC{Holmes-Wong2017-ti,\n  title           = "{Unlocking Maps for Discovery and Other Purpose}",\n  author          = "Holmes-Wong, D and Chiang, Y Y",\n  year            =  2017,\n  howpublished    = "Digital Library Federation (DLF) Forum",\n  location        = "Pittsburg, PA, USA"\n}\n\n
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\n \n\n \n \n \n \n \n \n Automatic alignment of geographic features in contemporary vector data and historical maps.\n \n \n \n \n\n\n \n Duan, W.; Chiang, Y.; Knoblock, C. A; Jain, V.; Feldman, D.; Uhl, J. H; and Leyk, S.\n\n\n \n\n\n\n In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, of GeoAI '17, pages 45–54, New York, NY, USA, November 2017. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n \n \"Automatic-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Duan2017-de,\n  title     = "{Automatic alignment of geographic features in contemporary\n               vector data and historical maps}",\n  booktitle = "{Proceedings of the 1st Workshop on Artificial Intelligence and\n               Deep Learning for Geographic Knowledge Discovery}",\n  author    = "Duan, Weiwei and Chiang, Yao-Yi and Knoblock, Craig A and Jain,\n               Vinil and Feldman, Dan and Uhl, Johannes H and Leyk, Stefan",\n  publisher = "Association for Computing Machinery",\n  pages     = "45--54",\n  series    = "GeoAI '17",\n  month     =  nov,\n  year      =  2017,\n  url       = "https://doi.org/10.1145/3149808.3149816",\n  url-file      = "papers/Duan-et-al.-2017-Automatic-alignment-of-geographic-features-in-contemporary-vector-data-and-historical-maps.pdf",\n  address   = "New York, NY, USA",\n  location  = "Los Angeles, California",\n  isbn      = "9781450354981",\n  doi       = "10.1145/3149808.3149816"\n}\n\n
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\n \n\n \n \n \n \n \n Machine-learning based Approaches for Extracting Settlement Features from Historical Maps.\n \n \n \n\n\n \n Uhl, J. H; Leyk, S.; Chiang, Y.; Duan, W.; and Knoblock, C. A\n\n\n \n\n\n\n The International Land Use Symposium 2017 (Spatial data modelling and visualisation to enlighten sustainable policy making), 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@MISC{Uhl2017-im,\n  title        = "{Machine-learning based Approaches for Extracting Settlement\n                  Features from Historical Maps}",\n  author       = "Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\n                  Weiwei and Knoblock, Craig A",\n  year         =  2017,\n  howpublished = "The International Land Use Symposium 2017 (Spatial data\n                  modelling and visualisation to enlighten sustainable policy\n                  making)",\n  location     = "Dresden, Germany"\n}\n\n
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\n \n\n \n \n \n \n \n \n Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution.\n \n \n \n \n\n\n \n Lin, Y.; Chiang, Y.; Pan, F.; Stripelis, D.; Ambite, J. L.; Eckel, S. P; and Habre, R.\n\n\n \n\n\n\n In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, volume 8, pages 1–10, New York, NY, USA, November 2017. ACM\n \n\n\n\n
\n\n\n\n \n \n \"MiningPaper\n  \n \n \n \"Mining-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Lin2017-go,\n  title      = "{Mining Public Datasets for Modeling Intra-City PM2.5\n                Concentrations at a Fine Spatial Resolution}",\n  booktitle  = "{Proceedings of the 25th ACM SIGSPATIAL International Conference\n                on Advances in Geographic Information Systems}",\n  author     = "Lin, Yijun and Chiang, Yao-Yi and Pan, Fan and Stripelis,\n                Dimitrios and Ambite, Jose Luis and Eckel, Sandrah P and Habre,\n                Rima",\n  publisher  = "ACM",\n  volume     =  8,\n  pages      = "1--10",\n  month      =  nov,\n  year       =  2017,\n  url        = "https://dl.acm.org/doi/10.1145/3139958.3140013",\n  url-file       = "papers/Lin-et-al.-2017-Mining-Public-Datasets-for-Modeling-Intra-City-PM2.5-Concentrations-at-a-Fine-Spatial-Resolution.pdf",\n  address    = "New York, NY, USA",\n  language   = "en",\n  conference = "SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on\n                Advances in Geographic Information Systems",\n  location   = "Redondo Beach CA USA",\n  isbn       = "9781450354905",\n  pmid       = "29527599",\n  doi        = "10.1145/3139958.3140013",\n  pmc        = "PMC5841919"\n}\n\n
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\n \n\n \n \n \n \n \n \n A Scalable Data Integration and Analysis Architecture for Sensor Data of Pediatric Asthma.\n \n \n \n \n\n\n \n Stripelis, D; Ambite, J L; Chiang, Y Y; Eckel, S P; and Habre, R\n\n\n \n\n\n\n In Proceedings of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pages 1407–1408, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Stripelis2017-ab,\n  title     = "{{A Scalable Data Integration and Analysis Architecture for\n               Sensor Data of Pediatric Asthma}}",\n  booktitle = "{Proceedings of the 2017 IEEE 33rd International Conference on\n               Data Engineering (ICDE)}",\n  author    = "Stripelis, D and Ambite, J L and Chiang, Y Y and Eckel, S P and\n               Habre, R",\n  pages     = "1407--1408",\n  year      =  2017,\n  url       = "http://dx.doi.org/10.1109/ICDE.2017.198",\n  url-file      = "papers/Stripelis-et-al.-2017-A-Scalable-Data-Integration-and-Analysis-Architecture-for-Sensor-Data-of-Pediatric-Asthma.pdf",\n  doi       = "10.1109/ICDE.2017.198"\n}\n\n
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\n \n\n \n \n \n \n \n \n Unlocking Textual Content from Historical Maps - Potentials and Applications, Trends, and Outlooks.\n \n \n \n \n\n\n \n Chiang, Y.\n\n\n \n\n\n\n In Santosh, K C; Hangarge, M.; Bevilacqua, V.; and Negi, A., editor(s), Recent Trends in Image Processing and Pattern Recognition, volume 709, pages 111–124. Springer Singapore, 2017.\n \n\n\n\n
\n\n\n\n \n \n \"Unlocking-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@INCOLLECTION{Chiang2017-ym,\n  title     = "{Unlocking Textual Content from Historical Maps - Potentials and\n               Applications, Trends, and Outlooks}",\n  booktitle = "{Recent Trends in Image Processing and Pattern Recognition}",\n  author    = "Chiang, Yao-Yi",\n  editor    = "Santosh, K C and Hangarge, Mallikarjun and Bevilacqua,\n               Vitoantonio and Negi, Atul",\n  publisher = "Springer Singapore",\n  volume    =  709,\n  pages     = "111--124",\n  year      =  2017,\n  url-file      = "papers/Chiang-2017-Unlocking-Textual-Content-from-Historical-Maps-Potentials-and-Applications,-Trends,-and-Outlooks.pdf",\n  isbn      = "9789811048593"\n}\n\n
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\n \n\n \n \n \n \n \n \n Extracting human settlement footprint from historical topographic map series using context-based machine learning.\n \n \n \n \n\n\n \n Uhl, J. H; Leyk, S.; Chiang, Y.; Duan, W.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the 8th ​​International Conference of Pattern Recognition Systems (ICPRS 2017), pages 15 (6 .)–15 (6 .), January 2017. IET Digital Library\n \n\n\n\n
\n\n\n\n \n \n \"ExtractingPaper\n  \n \n \n \"Extracting-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Uhl2017-ug,\n  title     = "{Extracting human settlement footprint from historical\n               topographic map series using context-based machine learning}",\n  booktitle = "{Proceedings of the 8th ​​International Conference of Pattern\n               Recognition Systems (ICPRS 2017)}",\n  author    = "Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\n               Weiwei and Knoblock, Craig A",\n  publisher = "IET Digital Library",\n  pages     = "15 (6 .)--15 (6 .)",\n  month     =  jan,\n  year      =  2017,\n  url       = "https://digital-library.theiet.org/content/conferences/10.1049/cp.2017.0144",\n  url-file      = "papers/Uhl-et-al.-2017-Extracting-human-settlement-footprint-from-historical-topographic-map-series-using-context-based-machine-learning.pdf",\n  doi       = "10.1049/cp.2017.0144"\n}\n\n
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\n \n\n \n \n \n \n \n LINKING HISTORICAL MAPS TO USC SHOAH FOUNDATION VISUAL HISTORY ARCHIVE .\n \n \n \n\n\n \n Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 28th International Cartographic Conference, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2017-vb,\n  title     = "{{LINKING HISTORICAL MAPS TO USC SHOAH FOUNDATION VISUAL HISTORY\n               ARCHIVE }}",\n  booktitle = "{Proceedings of the 28th International Cartographic Conference}",\n  author    = "Chiang, Yao-Yi",\n  year      =  2017\n}\n\n
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\n  \n 2016\n \n \n (9)\n \n \n
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\n \n\n \n \n \n \n \n Exploiting Context in Cartographic Evolutionary Documents to Extract and Build Linked Spatial-Temporal Datasets.\n \n \n \n\n\n \n Chiang, Y Y\n\n\n \n\n\n\n 2016 Conference on Complex Systems, Complex Systems Society, 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@MISC{Chiang2016-hc,\n  title        = "{Exploiting Context in Cartographic Evolutionary Documents to\n                  Extract and Build Linked Spatial-Temporal Datasets}",\n  author       = "Chiang, Y Y",\n  year         =  2016,\n  howpublished = "2016 Conference on Complex Systems, Complex Systems Society",\n  location     = "Amsterdam, Netherlands"\n}\n\n
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\n \n\n \n \n \n \n \n \n Q2P: Discovering Query Templates via Autocompletion.\n \n \n \n \n\n\n \n Wu, W.; Meng, W.; Su, W.; Zhou, G.; and Chiang, Y.\n\n\n \n\n\n\n ACM Trans. Web, 10(2): 1–29. April 2016.\n \n\n\n\n
\n\n\n\n \n \n \"Q2P:Paper\n  \n \n \n \"Q2P:-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@ARTICLE{Wu2016-io,\n  title     = "{Q2P: Discovering Query Templates via Autocompletion}",\n  author    = "Wu, Wensheng and Meng, Weiyi and Su, Weifeng and Zhou, Guangyou\n               and Chiang, Yao-Yi",\n  journal   = "ACM Trans. Web",\n  publisher = "Association for Computing Machinery",\n  volume    =  10,\n  number    =  2,\n  pages     = "1--29",\n  month     =  apr,\n  year      =  2016,\n  url       = "https://doi.org/10.1145/2873061",\n  url-file      = "papers/Wu-et-al.-2016-Q2P-Discovering-Query-Templates-via-Autocompletion.pdf",\n  address   = "New York, NY, USA",\n  issn      = "1559-1131",\n  doi       = "10.1145/2873061"\n}\n\n
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\n \n\n \n \n \n \n \n Hot Spots of Tweets Related to Food, Entertainment, Work, and Study in Gangnam Area of Seoul, Korea.\n \n \n \n\n\n \n Park, W; Chiang, Y.; Lee, S J; and Yu, K\n\n\n \n\n\n\n In Esri Map Book, volume 31: GIS – Enabling a Smarter World. Redlands. Esri, 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INCOLLECTION{Park2016-ht,\n  title     = "{Hot Spots of Tweets Related to Food, Entertainment, Work, and\n               Study in Gangnam Area of Seoul, Korea}",\n  booktitle = "{Esri Map Book}",\n  author    = "Park, W and Chiang, Y-Y and Lee, S J and Yu, K",\n  publisher = "Esri",\n  volume    = "31: GIS -- Enabling a Smarter World. Redlands",\n  year      =  2016\n}\n\n
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\n \n\n \n \n \n \n \n \n Building knowledge graph from public data for predictive analysis: a case study on predicting technology future in space and time.\n \n \n \n \n\n\n \n Duan, W.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, pages 7–13, October 2016. ACM\n \n\n\n\n
\n\n\n\n \n \n \"BuildingPaper\n  \n \n \n \"Building-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Duan2016-se,\n  title     = "{{Building knowledge graph from public data for predictive\n               analysis: a case study on predicting technology future in space\n               and time}}",\n  booktitle = "{{Proceedings of the 5th ACM SIGSPATIAL International Workshop on\n               Analytics for Big Geospatial Data}}",\n  author    = "Duan, Weiwei and Chiang, Yao-Yi",\n  publisher = "ACM",\n  pages     = "7--13",\n  month     =  oct,\n  year      =  2016,\n  url       = "http://dl.acm.org/citation.cfm?doid=3006386.3006388",\n  url-file      = "papers/Duan-and-Chiang-2016-Building-knowledge-graph-from-public-data-for-pre...-ysis-a-case-study-on-predicting-technology-future-in-space-and-time.pdf",\n  isbn      = "9781450345811",\n  doi       = "10.1145/3006386.3006388"\n}\n\n
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\n \n\n \n \n \n \n \n \n Information Extraction based on the Concept of Geographic Context.\n \n \n \n \n\n\n \n Leyk, S.; and Chiang, Y.\n\n\n \n\n\n\n In Freundschuh, S. M, editor(s), Proceedings of the 2016 AutoCarto, pages 100–110, 2016. AutoCarto\n \n\n\n\n
\n\n\n\n \n \n \"InformationPaper\n  \n \n \n \"Information-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Leyk2016-dy,\n  title     = "{{Information Extraction based on the Concept of Geographic\n               Context}}",\n  booktitle = "{Proceedings of the 2016 AutoCarto}",\n  author    = "Leyk, Stefan and Chiang, Yao-Yi",\n  editor    = "Freundschuh, Scott M",\n  publisher = "AutoCarto",\n  pages     = "100--110",\n  year      =  2016,\n  url       = "http://geo.gmu.edu/AutoCarto2016/Leyk_and_Chiang.pdf",\n  url-file      = "papers/Leyk-and-Chiang-2016-Information-Extraction-based-on-the-Concept-of-Geographic-Context.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n An Automatic Approach for Building Place-Name Datasets from the Web.\n \n \n \n \n\n\n \n Zhang, Y.; Chiang, Y.; Knoblock, C.; Li, C.; Du, L.; Liu, S.; and Singh, S.\n\n\n \n\n\n\n In Proceedings of the 19th AGILE International Conference on Geographic Information Science, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"An-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Zhang2016-ra,\n  title     = "{{An Automatic Approach for Building Place-Name Datasets from the\n               Web}}",\n  booktitle = "{{Proceedings of the 19th AGILE International Conference on\n               Geographic Information Science}}",\n  author    = "Zhang, Ying and Chiang, Yao-Yi and Knoblock, Craig and Li,\n               Chaopeng and Du, Liming and Liu, Shaowen and Singh, Sanjay",\n  year      =  2016,\n  url-file      = "papers/Zhang-et-al.-2016-An-Automatic-Approach-for-Building-Place-Name-Datasets-from-the-Web.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Integrating Text Recognition for Overlapping Text Detection in Maps.\n \n \n \n \n\n\n \n Honarvar Nazari, N.; Tan, T. X.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the Electronic Imaging, Document Recognition and Retrieval XXIII conference, Society for Imaging Science and Technology, of 8, pages 1–8, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"Integrating-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Honarvar_Nazari2016-wv,\n  title     = "{Integrating Text Recognition for Overlapping Text Detection in\n               Maps}",\n  booktitle = "{Proceedings of the Electronic Imaging, Document Recognition and\n               Retrieval XXIII conference, Society for Imaging Science and\n               Technology}",\n  author    = "Honarvar Nazari, Narges and Tan, Tian Xiang and Chiang, Yao-Yi",\n  pages     = "1--8",\n  series    =  8,\n  year      =  2016,\n  url-file      = "papers/Honarvar-Nazari-et-al.-2016-Integrating-Text-Recognition-for-Overlapping-Text-Detection-in-Maps.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Assessing the impact of graphical quality on automatic text recognition in digital maps.\n \n \n \n \n\n\n \n Chiang, Y.; Leyk, S.; Nazari, N. H.; Moghaddam, S.; and Tan, T. X.\n\n\n \n\n\n\n Computers & geosciences, 93: 21–35. August 2016.\n \n\n\n\n
\n\n\n\n \n \n \"AssessingPaper\n  \n \n \n \"Assessing-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Chiang2016-zr,\n  title   = "{{Assessing the impact of graphical quality on automatic text\n             recognition in digital maps}}",\n  author  = "Chiang, Yao-Yi and Leyk, Stefan and Nazari, Narges Honarvar and\n             Moghaddam, Sima and Tan, Tian Xiang",\n  journal = "Computers \\& geosciences",\n  volume  =  93,\n  pages   = "21--35",\n  month   =  aug,\n  year    =  2016,\n  url     = "http://dx.doi.org/10.1016/j.cageo.2016.04.013",\n  url-file    = "papers/Chiang-et-al.-2016-Assessing-the-impact-of-graphical-quality-on-automatic-text-recognition-in-digital-maps.pdf",\n  issn    = "0098-3004",\n  doi     = "10.1016/j.cageo.2016.04.013"\n}\n\n
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\n \n\n \n \n \n \n \n \n Recognizing Text On Historical Maps Using Maps From Multiple Time Periods.\n \n \n \n \n\n\n \n Yu, R.; Luo, Z.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 23rd International Conference on Pattern Recognition, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"Recognizing-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Yu2016-mk,\n  title     = "{{Recognizing Text On Historical Maps Using Maps From Multiple\n               Time Periods}}",\n  booktitle = "{{Proceedings of the 23rd International Conference on Pattern\n               Recognition}}",\n  author    = "Yu, Ronald and Luo, Zexuan and Chiang, Yao-Yi",\n  year      =  2016,\n  url-file      = "papers/Yu-et-al.-2016-Recognizing-Text-On-Historical-Maps-Using-Maps-From-Multiple-Time-Periods.pdf"\n}\n\n
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\n  \n 2015\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n Creating an Intuitive and Effective User Interface for Map Processing in a Geographic Information System.\n \n \n \n\n\n \n Fernandes, R.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 27th International Cartographic Conference, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Fernandes2015-fr,\n  title     = "{{{Creating an Intuitive and Effective User Interface for Map\n               Processing in a Geographic Information System}}}",\n  booktitle = "{{Proceedings of the 27th International Cartographic Conference}}",\n  author    = "Fernandes, Renuka and Chiang, Yao-Yi",\n  year      =  2015,\n  isbn      = "9788588783119"\n}\n\n
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\n \n\n \n \n \n \n \n Semi-Automated Visualization of Spatial Context in Unstructured Text.\n \n \n \n\n\n \n Chiang, Y.; and Gehring, S.\n\n\n \n\n\n\n In Proceedings of the 27th International Cartographic Conference, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2015-xq,\n  title     = "{{Semi-Automated Visualization of Spatial Context in Unstructured\n               Text}}",\n  booktitle = "{{Proceedings of the 27th International Cartographic Conference}}",\n  author    = "Chiang, Yao-Yi and Gehring, Sarah",\n  year      =  2015,\n  isbn      = "9788588783119"\n}\n\n
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\n \n\n \n \n \n \n \n \n Querying Historical Maps As a Unified, Structured, and Linked Spatiotemporal Source: Vision Paper.\n \n \n \n \n\n\n \n Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, of GIS '15, pages 16:1–16:4, New York, NY, USA, November 2015. ACM\n \n\n\n\n
\n\n\n\n \n \n \"QueryingPaper\n  \n \n \n \"Querying-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2015-tr,\n  title     = "{{Querying Historical Maps As a Unified, Structured, and Linked\n               Spatiotemporal Source: Vision Paper}}",\n  booktitle = "{{Proceedings of the 23rd SIGSPATIAL International Conference on\n               Advances in Geographic Information Systems}}",\n  author    = "Chiang, Yao-Yi",\n  publisher = "ACM",\n  pages     = "16:1--16:4",\n  series    = "GIS '15",\n  month     =  nov,\n  year      =  2015,\n  url       = "http://doi.acm.org/10.1145/2820783.2820887",\n  url-file      = "papers/Chiang-2015-Querying-Historical-Maps-As-a-Unified,-Structured,-and-Linked-Spatiotemporal-Source-Vision-Paper.pdf",\n  address   = "New York, NY, USA",\n  location  = "Bellevue, Washington",\n  isbn      = "9781450339674",\n  doi       = "10.1145/2820783.2820887"\n}\n\n
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\n \n\n \n \n \n \n \n Exploiting Online Gazetteer for Fully Automatic Extraction of Cartographic Symbols.\n \n \n \n\n\n \n Chiang, Y.; and Leyk, S.\n\n\n \n\n\n\n In Proceedings of the 27th International Cartographic Conference, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2015-bm,\n  title     = "{{Exploiting Online Gazetteer for Fully Automatic Extraction of\n               Cartographic Symbols}}",\n  booktitle = "{{Proceedings of the 27th International Cartographic Conference}}",\n  author    = "Chiang, Yao-Yi and Leyk, Stefan",\n  year      =  2015,\n  isbn      = "9788588783119"\n}\n\n% The entry below contains non-ASCII chars that could not be converted\n% to a LaTeX equivalent.\n
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\n \n\n \n \n \n \n \n \n Visualizing Land Reclamation in Hong Kong: A Web Application.\n \n \n \n \n\n\n \n Ngo, V.; Swift, J.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 27th International Cartographic Conference, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"Visualizing-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Ngo2015-yj,\n  title     = "{{Visualizing Land Reclamation in Hong Kong: A Web Application}}",\n  booktitle = "{{Proceedings of the 27th International Cartographic Conference}}",\n  author    = "Ngo, Vivian and Swift, Jennifer and Chiang, Yao-Yi",\n  year      =  2015,\n  url-file      = "papers/Ngo-et-al.-2015-Visualizing-Land-Reclamation-in-Hong-Kong-A-Web-Application.pdf",\n  isbn      = "9788588783119"\n}\n\n
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\n  \n 2014\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n \n A Survey of Digital Map Processing Techniques.\n \n \n \n \n\n\n \n Chiang, Y.; Leyk, S.; and Knoblock, C. A\n\n\n \n\n\n\n ACM Comput. Surv., 47(1): 1–44. May 2014.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 31 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Chiang2014-gr,\n  title     = "{A Survey of Digital Map Processing Techniques}",\n  author    = "Chiang, Yao-Yi and Leyk, Stefan and Knoblock, Craig A",\n  journal   = "ACM Comput. Surv.",\n  publisher = "Association for Computing Machinery",\n  volume    =  47,\n  number    =  1,\n  pages     = "1--44",\n  month     =  may,\n  year      =  2014,\n  url       = "https://doi.org/10.1145/2557423",\n  url-file      = "papers/Chiang-et-al.-2014-A-Survey-of-Digital-Map-Processing-Techniques.pdf",\n  address   = "New York, NY, USA",\n  issn      = "0360-0300",\n  doi       = "10.1145/2557423"\n}\n\n
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\n \n\n \n \n \n \n \n \n A system for efficient cleaning and transformation of geospatial data attributes.\n \n \n \n \n\n\n \n Chiang, Y.; Wu, B.; Anand, A.; Akade, K.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '14, pages 577–580, New York, NY, USA, November 2014. ACM\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2014-ij,\n  title     = "{A system for efficient cleaning and transformation of geospatial\n               data attributes}",\n  booktitle = "{Proceedings of the 22nd ACM SIGSPATIAL International Conference\n               on Advances in Geographic Information Systems}",\n  author    = "Chiang, Yao-Yi and Wu, Bo and Anand, Akshay and Akade, Ketan and\n               Knoblock, Craig A",\n  publisher = "ACM",\n  pages     = "577--580",\n  series    = "SIGSPATIAL '14",\n  month     =  nov,\n  year      =  2014,\n  url       = "https://dl.acm.org/citation.cfm?doid=2666310.2666373",\n  url-file      = "papers/Chiang-et-al.-2014-A-system-for-efficient-cleaning-and-transformation-of-geospatial-data-attributes.pdf",\n  address   = "New York, NY, USA",\n  location  = "Dallas, Texas",\n  isbn      = "9781450331319",\n  doi       = "10.1145/2666310.2666373"\n}\n\n
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\n \n\n \n \n \n \n \n \n A parallel query engine for interactive spatiotemporal analysis.\n \n \n \n \n\n\n \n Sathe, M.; Knoblock, C. A; Chiang, Y.; and Harris, A.\n\n\n \n\n\n\n In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '14, pages 429–432, New York, NY, USA, November 2014. ACM\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 14 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Sathe2014-uo,\n  title     = "{A parallel query engine for interactive spatiotemporal analysis}",\n  booktitle = "{Proceedings of the 22nd ACM SIGSPATIAL International Conference\n               on Advances in Geographic Information Systems}",\n  author    = "Sathe, Mihir and Knoblock, Craig A and Chiang, Yao-Yi and\n               Harris, Aaron",\n  publisher = "ACM",\n  pages     = "429--432",\n  series    = "SIGSPATIAL '14",\n  month     =  nov,\n  year      =  2014,\n  url       = "https://dl.acm.org/citation.cfm?doid=2666310.2666437",\n  url-file      = "papers/Sathe-et-al.-2014-A-parallel-query-engine-for-interactive-spatiotemporal-analysis.pdf",\n  address   = "New York, NY, USA",\n  location  = "Dallas, Texas",\n  isbn      = "9781450331319",\n  doi       = "10.1145/2666310.2666437"\n}\n\n
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\n \n\n \n \n \n \n \n \n \\textbfA Training-by-Example Approach for Symbol Spotting from Raster Maps .\n \n \n \n \n\n\n \n Chiang, Y.; Chioh, P.; and Moghaddam, S.\n\n\n \n\n\n\n In Proceedings of the 8th International Conference on Geographic Information Science, pages 264–269, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"\\textbfAPaper\n  \n \n \n \"\\textbfA-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2014-hg,\n  title     = "{{{\\textbf{A Training-by-Example Approach for Symbol Spotting\n               from Raster Maps }}}}",\n  booktitle = "{{Proceedings of the 8th International Conference on Geographic\n               Information Science}}",\n  author    = "Chiang, Yao-Yi and Chioh, Phokgoan and Moghaddam, Sima",\n  pages     = "264--269",\n  year      =  2014,\n  url       = "http://www.giscience.org/download/proceedings/GIScience2014EA.pdf",\n  url-file      = "papers/Chiang-et-al.-2014-A-Training-by-Example-Approach-for-Symbol-Spotting-from-Raster-Maps.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n Location Prediction With Sparse GPS Data.\n \n \n \n\n\n \n Jaiswal, A.; Chiang, Y.; Knoblock, C. A; and Lan, L.\n\n\n \n\n\n\n In Proceedings of the 8th International Conference on Geographic Information Science, pages 315–219, 2014. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 15 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Jaiswal2014-km,\n  title     = "{{{Location Prediction With Sparse GPS Data}}}",\n  booktitle = "{{Proceedings of the 8th International Conference on Geographic\n               Information Science}}",\n  author    = "Jaiswal, Ayush and Chiang, Yao-Yi and Knoblock, Craig A and Lan,\n               Liang",\n  pages     = "315--219",\n  year      =  2014\n}\n\n
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\n \n\n \n \n \n \n \n \n Integration and Automation of Data Preparation and Data Mining.\n \n \n \n \n\n\n \n Narayanan, S; Jaiswal, A; Chiang, Y Y; Geng, Y; Knoblock, C A; and Szekely, P\n\n\n \n\n\n\n In Proceedings of the 2014 IEEE International Conference on Data Mining Workshop, pages 1076–1085, December 2014. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"IntegrationPaper\n  \n \n \n \"Integration-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Narayanan2014-cg,\n  title     = "{{{Integration and Automation of Data Preparation and Data\n               Mining}}}",\n  booktitle = "{Proceedings of the 2014 IEEE International Conference on Data\n               Mining Workshop}",\n  author    = "Narayanan, S and Jaiswal, A and Chiang, Y Y and Geng, Y and\n               Knoblock, C A and Szekely, P",\n  publisher = "IEEE",\n  pages     = "1076--1085",\n  month     =  dec,\n  year      =  2014,\n  url       = "http://dx.doi.org/10.1109/ICDMW.2014.44",\n  url-file      = "papers/Narayanan-et-al.-2014-Integration-and-Automation-of-Data-Preparation-and-Data-Mining.pdf",\n  language  = "English",\n  issn      = "2375-9232",\n  isbn      = "9781479942749",\n  doi       = "10.1109/ICDMW.2014.44"\n}\n\n
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\n \n\n \n \n \n \n \n \n Recognizing Text in Raster Maps.\n \n \n \n \n\n\n \n Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n GeoInformatica, 19(1): 1–27. February 2014.\n \n\n\n\n
\n\n\n\n \n \n \"RecognizingPaper\n  \n \n \n \"Recognizing-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 36 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Chiang2014-cu,\n  title     = "{{Recognizing Text in Raster Maps}}",\n  author    = "Chiang, Yao-Yi and Knoblock, Craig A",\n  journal   = "GeoInformatica",\n  publisher = "Springer US",\n  volume    =  19,\n  number    =  1,\n  pages     = "1--27",\n  month     =  feb,\n  year      =  2014,\n  url       = "http://dx.doi.org/10.1007/s10707-014-0203-9",\n  url-file      = "papers/Chiang-and-Knoblock-2014-Recognizing-Text-in-Raster-Maps.pdf",\n  language  = "en",\n  issn      = "1384-6175, 1573-7624",\n  doi       = "10.1007/s10707-014-0203-9"\n}\n\n
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\n \n\n \n \n \n \n \n \n From Map Images to Geographic Names.\n \n \n \n \n\n\n \n Chiang, Y.; Moghaddam, S.; Gupta, S.; Fernandes, R.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 581–584, New York, New York, USA, November 2014. ACM\n \n\n\n\n
\n\n\n\n \n \n \"FromPaper\n  \n \n \n \"From-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 17 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2014-kx,\n  title     = "{{From Map Images to Geographic Names}}",\n  booktitle = "{{Proceedings of the 22nd ACM SIGSPATIAL International Conference\n               on Advances in Geographic Information Systems}}",\n  author    = "Chiang, Yao-Yi and Moghaddam, Sima and Gupta, Sanjauli and\n               Fernandes, Renuka and Knoblock, Craig A",\n  publisher = "ACM",\n  pages     = "581--584",\n  month     =  nov,\n  year      =  2014,\n  url       = "http://dl.acm.org/citation.cfm?doid=2666310.2666374",\n  url-file      = "papers/Chiang-et-al.-2014-From-Map-Images-to-Geographic-Names.pdf",\n  address   = "New York, New York, USA",\n  isbn      = "9781450331319",\n  doi       = "10.1145/2666310.2666374"\n}\n\n
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\n  \n 2013\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n A semantic approach to retrieving, linking, and integrating heterogeneous geospatial data.\n \n \n \n \n\n\n \n Zhang, Y.; Chiang, Y.; Szekely, P.; and Knoblock, C. A\n\n\n \n\n\n\n In Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities, of AIIP '13, pages 31–37, New York, NY, USA, August 2013. ACM\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 93 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Zhang2013-fk,\n  title     = "{A semantic approach to retrieving, linking, and integrating\n               heterogeneous geospatial data}",\n  booktitle = "{{Joint Proceedings of the Workshop on AI Problems and Approaches\n               for Intelligent Environments and Workshop on Semantic Cities}}",\n  author    = "Zhang, Ying and Chiang, Yao-Yi and Szekely, Pedro and Knoblock,\n               Craig A",\n  publisher = "ACM",\n  pages     = "31--37",\n  series    = "AIIP '13",\n  month     =  aug,\n  year      =  2013,\n  url       = "https://dl.acm.org/citation.cfm?doid=2516911.2516914",\n  url-file      = "papers/Zhang-et-al.-2013-A-semantic-approach-to-retrieving,-linking,-and-integrating-heterogeneous-geospatial-data.pdf",\n  address   = "New York, NY, USA",\n  location  = "Beijing, China",\n  isbn      = "9781450323468",\n  doi       = "10.1145/2516911.2516914"\n}\n\n
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\n \n\n \n \n \n \n \n Strabo: A Complete System for Label Recognition in Maps.\n \n \n \n\n\n \n Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 26th International Cartographic Conference (ICC'13), pages 838–838, 2013. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2013-zo,\n  title     = "{{{Strabo: A Complete System for Label Recognition in Maps}}}",\n  booktitle = "{{Proceedings of the 26th International Cartographic Conference\n               (ICC'13)}}",\n  author    = "Chiang, Yao-Yi",\n  pages     = "838--838",\n  year      =  2013,\n  isbn      = "9781907075063"\n}\n\n
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\n \n\n \n \n \n \n \n \n Efficient and Robust Graphics Recognition from Historical Maps: 9th International Workshop, GREC 2011, Seoul, Korea, September 15-16, 2011, Revised Selected Papers.\n \n \n \n \n\n\n \n Chiang, Y.; Leyk, S.; and Knoblock, C. A\n\n\n \n\n\n\n In Kwon, Y.; and Ogier, J., editor(s), Graphics Recognition. New Trends and Challenges: 9th International Workshop, GREC 2011, Seoul, Korea, September 15-16, 2011, Revised Selected Papers, volume 7423, of Lecture Notes in Computer Science, GREC'11, pages 25–35. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013.\n \n\n\n\n
\n\n\n\n \n \n \"EfficientPaper\n  \n \n \n \"Efficient-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INCOLLECTION{Chiang2013-fw,\n  title     = "{Efficient and Robust Graphics Recognition from Historical Maps:\n               9th International Workshop, GREC 2011, Seoul, Korea, September\n               15-16, 2011, Revised Selected Papers}",\n  booktitle = "{{Graphics Recognition. New Trends and Challenges: 9th\n               International Workshop, GREC 2011, Seoul, Korea, September\n               15-16, 2011, Revised Selected Papers}}",\n  author    = "Chiang, Yao-Yi and Leyk, Stefan and Knoblock, Craig A",\n  editor    = "Kwon, Young-Bin and Ogier, Jean-Marc",\n  publisher = "Springer Berlin Heidelberg",\n  volume    =  7423,\n  pages     = "25--35",\n  series    = "Lecture Notes in Computer Science, GREC'11",\n  year      =  2013,\n  url       = "http://dx.doi.org/10.1007/978-3-642-36824-0_3",\n  url-file      = "papers/Chiang-et-al.-2013-Efficient-and-Robust-Graphics-Recognition-from-Hist...-EC-2011,-Seoul,-Korea,-September-15-16,-2011,-Revised-Selected-Papers.pdf",\n  address   = "Berlin, Heidelberg",\n  location  = "Seoul, Korea",\n  issn      = "0302-9743, 1611-3349",\n  isbn      = "9783642368233, 9783642368240",\n  doi       = "10.1007/978-3-642-36824-0\\_3"\n}\n\n
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\n \n\n \n \n \n \n \n \n A General Approach for Extracting Road Vector Data from Raster Maps.\n \n \n \n \n\n\n \n Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n International Journal on Document Analysis and Recognition (IJDAR), 16(1): 55–81. March 2013.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Chiang2013-gv,\n  title     = "{{A General Approach for Extracting Road Vector Data from Raster\n               Maps}}",\n  author    = "Chiang, Yao-Yi and Knoblock, Craig A",\n  journal   = "International Journal on Document Analysis and Recognition\n               (IJDAR)",\n  publisher = "Springer, Berlin / Heidelberg",\n  volume    =  16,\n  number    =  1,\n  pages     = "55--81",\n  month     =  mar,\n  year      =  2013,\n  url       = "http://link.springer.com/10.1007/s10032-011-0177-1",\n  url-file      = "papers/Chiang-and-Knoblock-2013-A-General-Approach-for-Extracting-Road-Vector-Data-from-Raster-Maps.pdf",\n  language  = "English",\n  issn      = "1433-2833, 1433-2825",\n  doi       = "10.1007/s10032-011-0177-1"\n}\n\n
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\n  \n 2012\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Generating Named Road Vector Data from Raster Maps.\n \n \n \n \n\n\n \n Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n In Xiao, N.; Kwan, M.; Goodchild, M. F; and Shekhar, S., editor(s), Geographic Information Science, volume 7478, of Lecture Notes in Computer Science, pages 57–71. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.\n \n\n\n\n
\n\n\n\n \n \n \"GeneratingPaper\n  \n \n \n \"Generating-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 26 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INCOLLECTION{Chiang2012-dp,\n  title     = "{Generating Named Road Vector Data from Raster Maps}",\n  booktitle = "{Geographic Information Science}",\n  author    = "Chiang, Yao-Yi and Knoblock, Craig A",\n  editor    = "Xiao, Ningchuan and Kwan, Mei-Po and Goodchild, Michael F and\n               Shekhar, Shashi",\n  publisher = "Springer Berlin Heidelberg",\n  volume    =  7478,\n  pages     = "57--71",\n  series    = "Lecture Notes in Computer Science",\n  year      =  2012,\n  url       = "http://dx.doi.org/10.1007/978-3-642-33024-7_5",\n  url-file      = "papers/Chiang-and-Knoblock-2012-Chiang-2012-Generating-Named-Road-Vector-Data.pdf",\n  address   = "Berlin, Heidelberg",\n  language  = "English",\n  issn      = "0302-9743, 1611-3349",\n  isbn      = "9783642330230, 9783642330247",\n  doi       = "10.1007/978-3-642-33024-7\\_5"\n}\n
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\n  \n 2010\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Extracting Road Vector Data from Raster Maps.\n \n \n \n \n\n\n \n Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n In Ogier, J M; ., L. W; and J., L., editor(s), Graphics Recognition. Achievements, Challenges, and Evolution. GREC 2009. Lecture Notes in Computer Science, volume 6020, pages 93–105. Springer Berlin Heidelberg, 2010.\n \n\n\n\n
\n\n\n\n \n \n \"ExtractingPaper\n  \n \n \n \"Extracting-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INCOLLECTION{Chiang2010-rk,\n  title     = "{Extracting Road Vector Data from Raster Maps}",\n  booktitle = "{Graphics Recognition. Achievements, Challenges, and Evolution.\n               GREC 2009. Lecture Notes in Computer Science}",\n  author    = "Chiang, Yao-Yi and Knoblock, Craig A",\n  editor    = "Ogier, J M and ., Liu W and J., Llad{\\'o}s",\n  publisher = "Springer Berlin Heidelberg",\n  volume    =  6020,\n  pages     = "93--105",\n  year      =  2010,\n  url       = "http://dx.doi.org/10.1007/978-3-642-13728-0_9",\n  url-file      = "papers/Chiang-and-Knoblock-2010-Extracting-Road-Vector-Data-from-Raster-Maps.pdf",\n  doi       = "10.1007/978-3-642-13728-0\\_9"\n}\n\n
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\n \n\n \n \n \n \n \n \n A general approach to discovering, registering, and extracting features from raster maps.\n \n \n \n \n\n\n \n Knoblock, C. A; Chen, C.; Chiang, Y.; Goel, A.; Michelson, M.; and Shahabi, C.\n\n\n \n\n\n\n In Document Recognition and Retrieval XVII, volume 7534, pages 753402–753402–15, January 2010. International Society for Optics and Photonics\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Knoblock2010-vk,\n  title      = "{{A general approach to discovering, registering, and extracting\n                features from raster maps}}",\n  booktitle  = "{Document Recognition and Retrieval XVII}",\n  author     = "Knoblock, Craig A and Chen, Ching-Chien and Chiang, Yao-Yi and\n                Goel, Aman and Michelson, Matthew and Shahabi, Cyrus",\n  publisher  = "International Society for Optics and Photonics",\n  volume     =  7534,\n  pages      = "753402--753402--15",\n  month      =  jan,\n  year       =  2010,\n  url        = "http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=776640",\n  url-file       = "papers/Knoblock-et-al.-2010-A-general-approach-to-discovering,-registering,-and-extracting-features-from-raster-maps.pdf",\n  conference = "Document Recognition and Retrieval XVII",\n  doi        = "10.1117/12.838967"\n}\n\n
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\n \n\n \n \n \n \n \n \n An Approach for Recognizing Text Labels in Raster Maps.\n \n \n \n \n\n\n \n Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the 20th International Conference on Pattern Recognition, pages 3199–3202, 2010. \n \n\n\n\n
\n\n\n\n \n \n \"An-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2010-co,\n  title     = "{{{An Approach for Recognizing Text Labels in Raster Maps}}}",\n  booktitle = "{{Proceedings of the 20th International Conference on Pattern\n               Recognition}}",\n  author    = "Chiang, Yao-Yi and Knoblock, Craig A",\n  pages     = "3199--3202",\n  year      =  2010,\n  url-file      = "papers/Chiang-and-Knoblock-2010-An-Approach-for-Recognizing-Text-Labels-in-Raster-Maps.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Strabo: a System for Extracting Road Vector Data From Raster Maps.\n \n \n \n \n\n\n \n Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 544–545, New York, New York, USA, November 2010. \\ ACM Request Permissions\n \n\n\n\n
\n\n\n\n \n \n \"Strabo:Paper\n  \n \n \n \"Strabo:-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2010-go,\n  title     = "{{Strabo: a System for Extracting Road Vector Data From Raster\n               Maps}}",\n  booktitle = "{{Proceedings of the 18th SIGSPATIAL International Conference on\n               Advances in Geographic Information Systems}}",\n  author    = "Chiang, Yao-Yi and Knoblock, Craig A",\n  publisher = "\\textbackslash ACM Request Permissions",\n  pages     = "544--545",\n  month     =  nov,\n  year      =  2010,\n  url       = "http://portal.acm.org/citation.cfm?doid=1869790.1869889",\n  url-file      = "papers/Chiang-and-Knoblock-2010-Strabo-a-System-for-Extracting-Road-Vector-Data-From-Raster-Maps.pdf",\n  address   = "New York, New York, USA",\n  isbn      = "9781450304283",\n  doi       = "10.1145/1869790.1869889"\n}\n\n
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\n  \n 2009\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Automatic and Accurate Extraction of Road Intersections from Raster Maps.\n \n \n \n \n\n\n \n Chiang, Y.; Knoblock., C. A; Shahabi, C.; and Chen, C.\n\n\n \n\n\n\n GeoInformatica, 13(2): 121–157. June 2009.\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n \n \"Automatic-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Chiang2009-xi,\n  title     = "{{{Automatic and Accurate Extraction of Road Intersections from\n               Raster Maps}}}",\n  author    = "Chiang, Yao-Yi and Knoblock., Craig A and Shahabi, Cyrus and\n               Chen, Ching-Chien",\n  journal   = "GeoInformatica",\n  publisher = "Springer US",\n  volume    =  13,\n  number    =  2,\n  pages     = "121--157",\n  month     =  jun,\n  year      =  2009,\n  url       = "https://doi.org/10.1007/s10707-008-0046-3",\n  url-file      = "papers/Chiang-et-al.-2009-Automatic-and-Accurate-Extraction-of-Road-Intersections-from-Raster-Maps.pdf",\n  language  = "en",\n  issn      = "1384-6175, 1573-7624",\n  doi       = "10.1007/s10707-008-0046-3"\n}\n\n
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\n \n\n \n \n \n \n \n \n A Method for Automatically Extracting Road Layers from Raster Maps.\n \n \n \n \n\n\n \n Chiang, Y Y; and Knoblock, C A\n\n\n \n\n\n\n In Proceedings of the 2009 10th International Conference on Document Analysis and Recognition, pages 838–842, July 2009. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2009-fh,\n  title     = "{{{A Method for Automatically Extracting Road Layers from Raster\n               Maps}}}",\n  booktitle = "{Proceedings of the 2009 10th International Conference on\n               Document Analysis and Recognition}",\n  author    = "Chiang, Y Y and Knoblock, C A",\n  pages     = "838--842",\n  month     =  jul,\n  year      =  2009,\n  url       = "http://dx.doi.org/10.1109/ICDAR.2009.274",\n  url-file      = "papers/Chiang-and-Knoblock-2009-A-Method-for-Automatically-Extracting-Road-Layers-from-Raster-Maps.pdf",\n  issn      = "1520-5363",\n  doi       = "10.1109/ICDAR.2009.274"\n}\n\n
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\n \n\n \n \n \n \n \n \n Classification of Raster Maps for Automatic Feature Extraction.\n \n \n \n \n\n\n \n Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, of GIS '09, pages 138–147, New York, NY, USA, November 2009. ACM\n \n\n\n\n
\n\n\n\n \n \n \"ClassificationPaper\n  \n \n \n \"Classification-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2009-dt,\n  title     = "{{Classification of Raster Maps for Automatic Feature Extraction}}",\n  booktitle = "{{Proceedings of the 17th ACM SIGSPATIAL International Conference\n               on Advances in Geographic Information Systems}}",\n  author    = "Chiang, Yao-Yi and Knoblock, Craig A",\n  publisher = "ACM",\n  pages     = "138--147",\n  series    = "GIS '09",\n  month     =  nov,\n  year      =  2009,\n  url       = "https://dl.acm.org/citation.cfm?doid=1653771.1653793",\n  url-file      = "papers/Chiang-and-Knoblock-2009-Classification-of-Raster-Maps-for-Automatic-Feature-Extraction.pdf",\n  address   = "New York, NY, USA",\n  location  = "Seattle, Washington",\n  isbn      = "9781605586496",\n  doi       = "10.1145/1653771.1653793"\n}\n\n
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\n  \n 2008\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Automatic extraction of road intersection position, connectivity, and orientations from raster maps.\n \n \n \n \n\n\n \n Chiang, Y.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 1–10, November 2008. ACM Press\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n \n \"Automatic-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2008-fo,\n  title     = "{{{Automatic extraction of road intersection position,\n               connectivity, and orientations from raster maps}}}",\n  booktitle = "{{Proceedings of the 16th ACM SIGSPATIAL International Conference\n               on Advances in Geographic Information Systems}}",\n  author    = "Chiang, Yao-Yi and Knoblock, Craig A",\n  publisher = "ACM Press",\n  pages     = "1--10",\n  month     =  nov,\n  year      =  2008,\n  url       = "http://doi.acm.org/10.1145/1463434.1463463",\n  url-file      = "papers/Chiang-and-Knoblock-2008-Automatic-extraction-of-road-intersection-position,-connectivity,-and-orientations-from-raster-maps.pdf",\n  isbn      = "9781605583235",\n  doi       = "10.1145/1463434.1463463"\n}\n\n
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\n  \n 2006\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Classification of Line and Character Pixels on Raster Maps Using Discrete Cosine Transformation Coefficients and Support Vector Machine.\n \n \n \n \n\n\n \n Chiang, Y. Y.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the International Conference on Pattern Recognition, volume 2, pages 1034–1037, August 2006. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"ClassificationPaper\n  \n \n \n \"Classification-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2006-ct,\n  title     = "{{{Classification of Line and Character Pixels on Raster Maps\n               Using Discrete Cosine Transformation Coefficients and Support\n               Vector Machine}}}",\n  booktitle = "{Proceedings of the International Conference on Pattern\n               Recognition}",\n  author    = "Chiang, Yao-Yi Yi and Knoblock, Craig A",\n  publisher = "IEEE",\n  volume    =  2,\n  pages     = "1034--1037",\n  month     =  aug,\n  year      =  2006,\n  url       = "http://dx.doi.org/10.1109/ICPR.2006.368",\n  url-file      = "papers/Chiang-and-Knoblock-2006-Classification-of-Line-and-Character-Pixels-o...-iscrete-Cosine-Transformation-Coefficients-and-Support-Vector-Machine.pdf",\n  issn      = "1051-4651",\n  isbn      = "9780769525211",\n  doi       = "10.1109/ICPR.2006.368"\n}\n\n
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\n \n\n \n \n \n \n \n \n Recognition of English Multi-oriented Characters.\n \n \n \n \n\n\n \n Pal, U; Kimura, F; Roy, K; and Pal, T\n\n\n \n\n\n\n In Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), volume 2, pages 873–876, August 2006. IEEE Computer Society\n \n\n\n\n
\n\n\n\n \n \n \"RecognitionPaper\n  \n \n \n \"Recognition-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Pal2006-bz,\n  title     = "{{{Recognition of English Multi-oriented Characters}}}",\n  booktitle = "{Proceedings of the 18th International Conference on Pattern\n               Recognition (ICPR'06)}",\n  author    = "Pal, U and Kimura, F and Roy, K and Pal, T",\n  publisher = "IEEE Computer Society",\n  volume    =  2,\n  pages     = "873--876",\n  month     =  aug,\n  year      =  2006,\n  url       = "http://dx.doi.org/10.1109/ICPR.2006.971",\n  url-file      = "papers/Pal-et-al.-2006-Recognition-of-English-Multi-oriented-Characters.pdf",\n  issn      = "1051-4651",\n  isbn      = "9780769525211",\n  doi       = "10.1109/ICPR.2006.971"\n}\n\n
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\n \n\n \n \n \n \n \n \n GEODEC: Eenableing Geospatial Decision Making.\n \n \n \n \n\n\n \n Shahabi, C.; Chiang, Y.; Chung, K.; Huang, K.; Khoshgozaran-Haghighi, J.; Craig A. Knoblocknd Lee, S. C.; Neumann, U.; Nevatia, R.; Rihan, A.; Thakkar, S.; and You, S.\n\n\n \n\n\n\n In Proceedings of the IEEE International Conference on Multimedia & Expo, pages 93–96, 2006. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"GEODEC:-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Shahabi2006-dp,\n  title     = "{{GEODEC: Eenableing Geospatial Decision Making}}",\n  booktitle = "{{Proceedings of the IEEE International Conference on Multimedia\n               \\& Expo}}",\n  author    = "Shahabi, Cyrus and Chiang, Yao-Yi and Chung, Kelvin and Huang,\n               Kai-Chen and Khoshgozaran-Haghighi, Jeff and Craig A. Knoblocknd\n               Lee, Sung Chun and Neumann, Ulrich and Nevatia, Ram and Rihan,\n               Arjun and Thakkar, Snehal and You, Suya",\n  publisher = "IEEE",\n  pages     = "93--96",\n  year      =  2006,\n  url-file      = "papers/Shahabi-et-al.-2006-GEODEC-Eenableing-Geospatial-Decision-Making.pdf"\n}\n\n
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\n  \n 2005\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Automatically identifying and georeferencing street maps on the web.\n \n \n \n \n\n\n \n Desai, S.; Knoblock, C. A; Chiang, Y.; Desai, K.; and Chen, C.\n\n\n \n\n\n\n In Proceedings of the 2nd International Workshop on Geographic Information Retrieval, pages 35–38, 2005. \n \n\n\n\n
\n\n\n\n \n \n \"Automatically-file\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Desai2005-gf,\n  title     = "{{{Automatically identifying and georeferencing street maps on\n               the web}}}",\n  booktitle = "{{Proceedings of the 2nd International Workshop on Geographic\n               Information Retrieval}}",\n  author    = "Desai, Sneha and Knoblock, Craig A and Chiang, Yao-Yi and Desai,\n               Kandarp and Chen, Ching-Chien",\n  pages     = "35--38",\n  year      =  2005,\n  url-file      = "papers/Desai-et-al.-2005-Automatically-identifying-and-georeferencing-street-maps-on-the-web.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Automatic extraction of road intersections from raster maps.\n \n \n \n \n\n\n \n Chiang, Y.; Knoblock, C. A; and Chen, C.\n\n\n \n\n\n\n In Proceedings of the 13th annual ACM international workshop on Geographic information systems, of GIS '05, pages 267–276, New York, NY, USA, November 2005. ACM\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n \n \"Automatic-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chiang2005-ny,\n  title     = "{Automatic extraction of road intersections from raster maps}",\n  booktitle = "{Proceedings of the 13th annual ACM international workshop on\n               Geographic information systems}",\n  author    = "Chiang, Yao-Yi and Knoblock, Craig A and Chen, Ching-Chien",\n  publisher = "ACM",\n  pages     = "267--276",\n  series    = "GIS '05",\n  month     =  nov,\n  year      =  2005,\n  url       = "https://dl.acm.org/citation.cfm?doid=1097064.1097102",\n  url-file      = "papers/Chiang-et-al.-2005-Automatic-extraction-of-road-intersections-from-raster-maps.pdf",\n  address   = "New York, NY, USA",\n  location  = "Bremen, Germany",\n  isbn      = "9781595931467",\n  doi       = "10.1145/1097064.1097102"\n}\n\n
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\n  \n 2004\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Automatically and Accurately Conflating Orthoimagery and Street Maps.\n \n \n \n \n\n\n \n Chen, C.; Knoblock, C. A; Shahabi, C.; Chiang, Y.; and Thakkar, S.\n\n\n \n\n\n\n In Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, of GIS '04, pages 47–56, New York, NY, USA, November 2004. ACM\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticallyPaper\n  \n \n \n \"Automatically-file\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@INPROCEEDINGS{Chen2004-tl,\n  title     = "{{{Automatically and Accurately Conflating Orthoimagery and\n               Street Maps}}}",\n  booktitle = "{{Proceedings of the 12th Annual ACM International Workshop on\n               Geographic Information Systems}}",\n  author    = "Chen, Ching-Chien and Knoblock, Craig A and Shahabi, Cyrus and\n               Chiang, Yao-Yi and Thakkar, Snehal",\n  publisher = "ACM",\n  pages     = "47--56",\n  series    = "GIS '04",\n  month     =  nov,\n  year      =  2004,\n  url       = "http://doi.acm.org/10.1145/1032222.1032231",\n  url-file      = "papers/Chen-et-al.-2004-Automatically-and-Accurately-Conflating-Orthoimagery-and-Street-Maps.pdf",\n  address   = "New York, NY, USA",\n  location  = "Washington DC, USA",\n  isbn      = "9781581139792",\n  doi       = "10.1145/1032222.1032231"\n}\n\n
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