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\n  \n 2026\n \n \n (3)\n \n \n
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\n \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 \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, 2026. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n \n \"LDTR: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 \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{10.1007/978-3-032-04617-8_3,\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  address = {Cham},\n  author = {Duan, Weiwei\nand Chiang, Yao-Yi\nand Knoblock, Craig A.},\n  booktitle = {Document Analysis and Recognition --  ICDAR 2025},\n  editor = {Yin, Xu-Cheng\nand Karatzas, Dimosthenis\nand Lopresti, Daniel},\n  isbn = {978-3-032-04617-8},\n  pages = {40--59},\n  publisher = {Springer Nature Switzerland},\n  title = {LDTR: Linear Object Detection Transformer for Accurate Graph Generation by Learning the N-Hop Connectivity Information},\n  url = {https://link.springer.com/chapter/10.1007/978-3-032-04617-8_3},\n  year = {2026}\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 \n LIGHT: Multi-modal Text Linking on Historical Maps.\n \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, 2026. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n \n \"LIGHT: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 \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{10.1007/978-3-032-04617-8_4,\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  address = {Cham},\n  author = {Lin, Yijun\nand Olson, Rhett\nand Wu, Junhan\nand Chiang, Yao-Yi\nand Weinman, Jerod},\n  booktitle = {Document Analysis and Recognition --  ICDAR 2025},\n  editor = {Yin, Xu-Cheng\nand Karatzas, Dimosthenis\nand Lopresti, Daniel},\n  isbn = {978-3-032-04617-8},\n  pages = {60--77},\n  publisher = {Springer Nature Switzerland},\n  title = {LIGHT: Multi-modal Text Linking on Historical Maps},\n  url = {https://link.springer.com/chapter/10.1007/978-3-032-04617-8_4},\n  year = {2026}\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 \n Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data.\n \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, 2026. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\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{10.1007/978-3-032-09530-5_26,\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  address = {Cham},\n  author = {Knoblock, Craig A.\nand Vu, Binh\nand Shbita, Basel\nand Chiang, Yao-Yi\nand Krishna, Pothula Punith\nand Lin, Xiao\nand Muric, Goran\nand Pyo, Jiyoon\nand Trejo-Sheu, Adriana\nand Ye, Meng},\n  booktitle = {The Semantic Web -- ISWC 2025},\n  editor = {Garijo, Daniel\nand Kirrane, Sabrina\nand Salatino, Angelo\nand Shimizu, Cogan\nand Acosta, Maribel\nand Nuzzolese, Andrea Giovanni\nand Ferrada, Sebasti{\\'a}n\nand Soulard, Thibaut\nand Kozaki, Kouji\nand Takeda, Hideaki\nand Gentile, Anna Lisa},\n  isbn = {978-3-032-09530-5},\n  pages = {451--471},\n  publisher = {Springer Nature Switzerland},\n  title = {Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data},\n  url = {https://link.springer.com/chapter/10.1007/978-3-032-09530-5_26},\n  year = {2026}\n}\n\n
\n
\n\n\n
\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 2025\n \n \n (26)\n \n \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. February 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\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{10.1016/j.jag.2025.104368,\n  author = {Mai, Gengchen and Xie, Yiqun and Jia, Xiaowei and Lao, Ni and Rao, Jinmeng and Zhu, Qing and Liu, Zeping and Chiang, Yao-Yi and Jiao, Junfeng},\n  doi = {10.1016/j.jag.2025.104368},\n  issn = {1569-8432},\n  journal = {International Journal of Applied Earth Observation and Geoinformation},\n  month = {February},\n  pages = {104368},\n  publisher = {Elsevier BV},\n  title = {Towards the next generation of Geospatial Artificial Intelligence},\n  url = {https://doi.org/10.1016/j.jag.2025.104368},\n  volume = {136},\n  year = {2025}\n}\n\n
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\n \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 \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. November 2025.\n \n\n\n\n
\n\n\n\n \n \n \"AI-SupportedPaper\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{10.1080/15420353.2025.2580239,\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  doi = {10.1080/15420353.2025.2580239},\n  issn = {1542-0361},\n  journal = {Journal of Map &amp; Geography Libraries},\n  month = {November},\n  pages = {1–23},\n  publisher = {Informa UK Limited},\n  title = {AI-Supported Smart Search for Multi-Period Historical Maps Using the mapKurator Place Name Index},\n  url = {https://doi.org/10.1080/15420353.2025.2580239},\n  year = {2025}\n}\n\n
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\n \n\n \n \n \n \n \n \n Exploiting Polygon Metadata to Recolor Historical Maps.\n \n \n \n \n\n\n \n Lin, F.; Knoblock, C. A.; Vu, B.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL ’25, pages 1122–1125, November 2025. ACM\n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\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{10.1145/3748636.3763209,\n  author = {Lin, Fandel and Knoblock, Craig A. and Vu, Binh and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  collection = {SIGSPATIAL ’25},\n  doi = {10.1145/3748636.3763209},\n  month = {November},\n  pages = {1122–1125},\n  publisher = {ACM},\n  series = {SIGSPATIAL ’25},\n  title = {Exploiting Polygon Metadata to Recolor Historical Maps},\n  url = {https://doi.org/10.1145/3748636.3763209},\n  year = {2025}\n}\n\n
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\n \n\n \n \n \n \n \n \n Exploiting Polygon Metadata to Colorize Draft Maps.\n \n \n \n \n\n\n \n Lin, F.; Knoblock, C. A.; Vu, B.; Shbita, B.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL ’25, pages 1126–1129, November 2025. ACM\n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\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{10.1145/3748636.3763210,\n  author = {Lin, Fandel and Knoblock, Craig A. and Vu, Binh and Shbita, Basel and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  collection = {SIGSPATIAL ’25},\n  doi = {10.1145/3748636.3763210},\n  month = {November},\n  pages = {1126–1129},\n  publisher = {ACM},\n  series = {SIGSPATIAL ’25},\n  title = {Exploiting Polygon Metadata to Colorize Draft Maps},\n  url = {https://doi.org/10.1145/3748636.3763210},\n  year = {2025}\n}\n\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 Lin, Y.; Chen, T.; Brungard, C.; Grunwald, S.; Ives, S.; Macander, M.; Nawrocki, T.; Chiang, Y.; and Jelinski, N.\n\n\n \n\n\n\n In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL ’25, pages 995–1007, November 2025. ACM\n \n\n\n\n
\n\n\n\n \n \n \"Fine-ScalePaper\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{10.1145/3748636.3764170,\n  author = {Lin, Yijun and Chen, Theresa and Brungard, Colby and Grunwald, Sabine and Ives, Sue and Macander, Matt and Nawrocki, Timm and Chiang, Yao-Yi and Jelinski, Nic},\n  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  collection = {SIGSPATIAL ’25},\n  doi = {10.1145/3748636.3764170},\n  month = {November},\n  pages = {995–1007},\n  publisher = {ACM},\n  series = {SIGSPATIAL ’25},\n  title = {Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning},\n  url = {https://doi.org/10.1145/3748636.3764170},\n  year = {2025}\n}\n\n
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\n \n\n \n \n \n \n \n \n Benchmarking Geospatial Question Answering with MapQA.\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 In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL ’25, pages 1042–1045, November 2025. ACM\n \n\n\n\n
\n\n\n\n \n \n \"BenchmarkingPaper\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 
@inproceedings{10.1145/3748636.3764174,\n  author = {Li, Zekun and Grossman, Malcolm and Qasemi, Ehsan and Kulkarni, Mihir and Chen, Muhao and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  collection = {SIGSPATIAL ’25},\n  doi = {10.1145/3748636.3764174},\n  month = {November},\n  pages = {1042–1045},\n  publisher = {ACM},\n  series = {SIGSPATIAL ’25},\n  title = {Benchmarking Geospatial Question Answering with MapQA},\n  url = {https://doi.org/10.1145/3748636.3764174},\n  year = {2025}\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 In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL ’25, pages 1056–1066, November 2025. ACM\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 
@inproceedings{10.1145/3748636.3764176,\n  author = {Namgung, Min and Lee, Janghyeon and Ding, Fangyi and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  collection = {SIGSPATIAL ’25},\n  doi = {10.1145/3748636.3764176},\n  month = {November},\n  pages = {1056–1066},\n  publisher = {ACM},\n  series = {SIGSPATIAL ’25},\n  title = {Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning},\n  url = {https://doi.org/10.1145/3748636.3764176},\n  year = {2025}\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.; Chiang, Y.; Chen, T.; Gerlek, M. P.; Jang, L.; Kirsanova, S.; Knoblock, C. A.; Lin, F.; Lin, Y.; Li, Z.; and Minton, S. N.\n\n\n \n\n\n\n In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL ’25, pages 717–728, November 2025. ACM\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 
@inproceedings{10.1145/3748636.3764602,\n  author = {Duan, Weiwei and Chiang, Yao-Yi and Chen, Theresa and Gerlek, Michael P. and Jang, Leeje and Kirsanova, Sofia and Knoblock, Craig A. and Lin, Fandel and Lin, Yijun and Li, Zekun and Minton, Steven N.},\n  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  collection = {SIGSPATIAL ’25},\n  doi = {10.1145/3748636.3764602},\n  month = {November},\n  pages = {717–728},\n  publisher = {ACM},\n  series = {SIGSPATIAL ’25},\n  title = {DIGMAPPER: A Modular System for Automated Geologic Map Digitization},\n  url = {https://doi.org/10.1145/3748636.3764602},\n  year = {2025}\n}\n\n
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\n \n\n \n \n \n \n \n \n MoVER: Modeling User Heterogeneity with Enriched Trajectory Representations for Human Mobility Prediction.\n \n \n \n \n\n\n \n Lin, Y.; Lin, F.; Kim, J.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL ’25, pages 1234–1237, November 2025. ACM\n \n\n\n\n
\n\n\n\n \n \n \"MoVER: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 
@inproceedings{10.1145/3748636.3771315,\n  author = {Lin, Yijun and Lin, Fandel and Kim, Jina and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  collection = {SIGSPATIAL ’25},\n  doi = {10.1145/3748636.3771315},\n  month = {November},\n  pages = {1234–1237},\n  publisher = {ACM},\n  series = {SIGSPATIAL ’25},\n  title = {MoVER: Modeling User Heterogeneity with Enriched Trajectory Representations for Human Mobility Prediction},\n  url = {https://doi.org/10.1145/3748636.3771315},\n  year = {2025}\n}\n\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, November 2025. ACM\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\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764914.3770888,\n  author = {Xie, Junyi and Kim, Jina and Chiang, Yao-Yi and Zhao, Lingyi and Shafique, Khurram},\n  booktitle = {Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection},\n  collection = {GeoAnomalies ’25},\n  doi = {10.1145/3764914.3770888},\n  month = {November},\n  pages = {56–59},\n  publisher = {ACM},\n  series = {GeoAnomalies ’25},\n  title = {BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data},\n  url = {https://doi.org/10.1145/3764914.3770888},\n  year = {2025}\n}\n\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, November 2025. ACM\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\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  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Generative and Agentic AI for Multi-Modality Space-Time Intelligence},\n  collection = {GeoGenAgent ’25},\n  doi = {10.1145/3764915.3770723},\n  month = {November},\n  pages = {49–53},\n  publisher = {ACM},\n  series = {GeoGenAgent ’25},\n  title = {HiCoTraj: Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory},\n  url = {https://doi.org/10.1145/3764915.3770723},\n  year = {2025}\n}\n\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 In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Human-Centered Geospatial Computing, of GeoHCC ’25, pages 10–14, November 2025. ACM\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
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@inproceedings{10.1145/3764917.3771333,\n  author = {Pyo, Jiyoon and Jiao, Yuankun and Chiang, Yao-Yi and Corey, Michael},\n  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Human-Centered Geospatial Computing},\n  collection = {GeoHCC ’25},\n  doi = {10.1145/3764917.3771333},\n  month = {November},\n  pages = {10–14},\n  publisher = {ACM},\n  series = {GeoHCC ’25},\n  title = {Augmenting Human-Centered Racial Covenant Detection and Georeferencing with Plug-and-Play NLP Pipelines},\n  url = {https://doi.org/10.1145/3764917.3771333},\n  year = {2025}\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. C.\n\n\n \n\n\n\n In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Human-Centered Geospatial Computing, of GeoHCC ’25, pages 15–19, November 2025. ACM\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 
@inproceedings{10.1145/3764917.3771334,\n  author = {Kim, Jina and Jang, Leeje and Chiang, Yao-Yi and Wang, Guanyu and Pasco, Michelle C.},\n  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Human-Centered Geospatial Computing},\n  collection = {GeoHCC ’25},\n  doi = {10.1145/3764917.3771334},\n  month = {November},\n  pages = {15–19},\n  publisher = {ACM},\n  series = {GeoHCC ’25},\n  title = {StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery},\n  url = {https://doi.org/10.1145/3764917.3771334},\n  year = {2025}\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, November 2025. ACM\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\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{10.1145/3764920.3770590,\n  author = {Kirsanova, Sofia and Duan, Weiwei and Chiang, YaoYi},\n  booktitle = {Proceedings of the 4th ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data},\n  collection = {GeoSearch ’25},\n  doi = {10.1145/3764920.3770590},\n  month = {November},\n  pages = {35–38},\n  publisher = {ACM},\n  series = {GeoSearch ’25},\n  title = {Detecting Legend Items on Historical Maps Using GPT-4o with In-Context Learning},\n  url = {https://doi.org/10.1145/3764920.3770590},\n  year = {2025}\n}\n\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, November 2025. ACM\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\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764925.3770911,\n  author = {Namgung, Min and Lee, JangHyeon and Lin, Yijun and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Urban Mobility Foundation Models},\n  collection = {UMFM ’25},\n  doi = {10.1145/3764925.3770911},\n  month = {November},\n  pages = {10–12},\n  publisher = {ACM},\n  series = {UMFM ’25},\n  title = {Capturing Shared and Unique Information in Multimodal Region Representations for Urban Mobility Prediction},\n  url = {https://doi.org/10.1145/3764925.3770911},\n  year = {2025}\n}\n\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, November 2025. ACM\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\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764926.3771941,\n  author = {Kim, Jina and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Urban-AI},\n  collection = {UrbanAI ’25},\n  doi = {10.1145/3764926.3771941},\n  month = {November},\n  pages = {94–95},\n  publisher = {ACM},\n  series = {UrbanAI ’25},\n  title = {Region Context from Unifying Points, Lines, and Polygons},\n  url = {https://doi.org/10.1145/3764926.3771941},\n  year = {2025}\n}\n\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, November 2025. ACM\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\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3764926.3771947,\n  author = {Namgung, Min and Chiang, Yao-Yi and Omitaomu, Olufemi A.},\n  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Urban-AI},\n  collection = {UrbanAI ’25},\n  doi = {10.1145/3764926.3771947},\n  month = {November},\n  pages = {27–36},\n  publisher = {ACM},\n  series = {UrbanAI ’25},\n  title = {CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health},\n  url = {https://doi.org/10.1145/3764926.3771947},\n  year = {2025}\n}\n\n
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\n \n\n \n \n \n \n \n \n Validating Machine Learning–Derived Built Environment Measures From Google Street View for Urban Aging Research in India.\n \n \n \n \n\n\n \n Atshan, S.; Namgung, M.; Lee, J.; Dhankhar, A.; Khobragade, P.; Cole, A.; Ailshire, J. A.; Adar, S. D.; Chiang, Y.; Lee, J.; and Nichols, E.\n\n\n \n\n\n\n SSRN Electronic Journal. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"ValidatingPaper\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{10.2139/ssrn.5943954,\n  author = {Atshan, Samer and Namgung, Min and Lee, Janghyeon and Dhankhar, Anushikha and Khobragade, Pranali and Cole, Aidan and Ailshire, Jennifer A. and Adar, Sara D. and Chiang, Yao-Yi and Lee, Jinkook and Nichols, Emma},\n  doi = {10.2139/ssrn.5943954},\n  issn = {1556-5068},\n  journal = {SSRN Electronic Journal},\n  publisher = {Elsevier BV},\n  title = {Validating Machine Learning–Derived Built Environment Measures From Google Street View for Urban Aging Research in India},\n  url = {https://doi.org/10.2139/ssrn.5943954},\n  year = {2025}\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 The SIGSPATIAL Special 15 (1), 2025.\n \n\n\n\n
\n\n\n\n \n \n \"GeoAnomalyPaper\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{Chiang2025_geoanomaly_detection_towards_finding_needles_of,\n  author = {Yao-Yi Chiang and Joon-Seok Kim and Cory Krause and Enrico Mattei and Khurram Shafique and Carola Wenk and Andreas Züfle},\n  howpublished = {The SIGSPATIAL Special 15 (1)},\n  title = {GeoAnomaly Detection: Towards finding Needles of Anomalous Behavior in a Haystack of Geospatial Data},\n  url = {https://dl.acm.org/doi/abs/10.1145/3757932.3757935},\n  year = {2025}\n}\n\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.; Sabine, G.; Brungard, C.; Chen, T.; and Lin, Y.\n\n\n \n\n\n\n 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 
@misc{Jelinski2025_finescale_soil_mapping_in_alaska_with,\n  author = {Nicolas A Jelinski and Yao-Yi Chiang and Timm Nawrocki and Matt Macander and Sue Ives and Grunwald Sabine and Colby Brungard and Theresa Chen and Yijun Lin},\n  howpublished = {ACM SIGSPATIAL 2025},\n  title = {Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning},\n  url = {https://experts.umn.edu/en/publications/fine-scale-soil-mapping-in-alaska-with-multimodal-machine-learnin/},\n  year = {2025}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems.\n \n \n \n \n\n\n \n Mokbel, M.; Shekar, S.; Züfle, A.; Chiang, Y.; Damiani, M. L.; and Youssef, M. A\n\n\n \n\n\n\n ACM, 2025.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\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{Mokbel2025_proceedings_of_the_33rd_acm_international,\n  author = {Mohamed Mokbel and Shashi Shekar and Andreas Züfle and Yao-Yi Chiang and Maria Luisa Damiani and Moustafa A Youssef},\n  howpublished = {ACM},\n  title = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  url = {https://par.nsf.gov/biblio/10653550},\n  year = {2025}\n}\n\n
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\n \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 \n Namgung, M.; Lin, Y.; Lee, J.; and Chiang, Y.\n\n\n \n\n\n\n arXiv, 2025.\n \n\n\n\n
\n\n\n\n \n \n \"LessPaper\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 
@misc{Namgung2025_less_is_more_multimodal_region_representation,\n  abstract = {With the increasing availability of geospatial datasets, researchers have explored region representation learning (RRL) to analyze complex region characteristics. Recent RRL methods use contrastive learning (CL) to capture shared information between two modalities but often overlook task-relevant unique information specific to each modality. Such modality-specific details can explain region characteristics that shared information alone cannot capture. Bringing information factorization to RRL can address this by factorizing multimodal data into shared and unique information. However, existing factorization approaches focus on two modalities, whereas RRL can benefit from various geospatial data. Extending factorization beyond two modalities is non-trivial because modeling high-order relationships introduces a combinatorial number of learning objectives, increasing model complexity. We introduce Cross modal Knowledge Injected Embedding, an information factorization approach for RRL that captures both shared and unique representations. CooKIE uses a pairwise inter-view learning approach that captures high-order information without modeling high-order dependency, avoiding exhaustive combinations. We evaluate CooKIE on three regression tasks and a land use classification task in New York City and Delhi, India. Results show that CooKIE outperforms existing RRL methods and a factorized RRL model, capturing multimodal information with fewer training parameters and floating-point operations per second (FLOPs). We release the code: https://github.com/MinNamgung/CooKIE.},\n  author = {Min Namgung and Yijun Lin and JangHyeon Lee and Yao-Yi Chiang},\n  howpublished = {arXiv},\n  primaryclass = {cs.LG},\n  title = {Less is More: Multimodal Region Representation via Pairwise Inter-view Learning},\n  url = {https://arxiv.org/api/RnQIRUtWQ/Dj2XGXX8a3TcyOxS0},\n  year = {2025}\n}\n\n
\n
\n\n\n
\n With the increasing availability of geospatial datasets, researchers have explored region representation learning (RRL) to analyze complex region characteristics. Recent RRL methods use contrastive learning (CL) to capture shared information between two modalities but often overlook task-relevant unique information specific to each modality. Such modality-specific details can explain region characteristics that shared information alone cannot capture. Bringing information factorization to RRL can address this by factorizing multimodal data into shared and unique information. However, existing factorization approaches focus on two modalities, whereas RRL can benefit from various geospatial data. Extending factorization beyond two modalities is non-trivial because modeling high-order relationships introduces a combinatorial number of learning objectives, increasing model complexity. We introduce Cross modal Knowledge Injected Embedding, an information factorization approach for RRL that captures both shared and unique representations. CooKIE uses a pairwise inter-view learning approach that captures high-order information without modeling high-order dependency, avoiding exhaustive combinations. We evaluate CooKIE on three regression tasks and a land use classification task in New York City and Delhi, India. Results show that CooKIE outperforms existing RRL methods and a factorized RRL model, capturing multimodal information with fewer training parameters and floating-point operations per second (FLOPs). We release the code: https://github.com/MinNamgung/CooKIE.\n
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\n \n\n \n \n \n \n \n \n Key research priorities in methodological approaches for measuring the exposome and studying its role in the development of dementia.\n \n \n \n \n\n\n \n Nichols, E.; Keller, K. P; Chang, H.; Chiang, Y.; Gross, A. L; Hayes‐Larson, E.; Henn, B. C.; Kezios, K. L; Meijer, E.; Shih, R. A; Szpiro, A. A; Weiss, J.; Weuve, J.; Adar, S. D; and Lee, J.\n\n\n \n\n\n\n Alzheimer's & Dementia 21 (11), 2025.\n \n\n\n\n
\n\n\n\n \n \n \"KeyPaper\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{Nichols2025_key_research_priorities_in_methodological_approaches,\n  author = {Emma Nichols and Kayleigh P Keller and Howard Chang and Yao‐Yi Chiang and Alden L Gross and Eleanor Hayes‐Larson and Birgit Claus Henn and Katrina L Kezios and Erik Meijer and Regina A Shih and Adam A Szpiro and Jordan Weiss and Jennifer Weuve and Sara D Adar and Jinkook Lee},\n  howpublished = {Alzheimer's & Dementia 21 (11)},\n  title = {Key research priorities in methodological approaches for measuring the exposome and studying its role in the development of dementia},\n  url = {https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1002/alz.70928},\n  year = {2025}\n}\n\n
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\n \n\n \n \n \n \n \n \n Modeling approaches for estimating the effects of risk factors using longitudinal lifecourse exposure data in dementia research.\n \n \n \n \n\n\n \n Nichols, E.; Bindas, A.; Atshan, S.; Chang, H.; Chiang, Y.; Henn, B. C.; Hayes‐Larson, E.; Keller, K. P; Kezios, K. L; Shih, R. A; Szpiro, A. A; Weiss, J.; Adar, S. D; Knapp, D. M; Lee, J.; and Weuve, J.\n\n\n \n\n\n\n Alzheimer's & Dementia 21 (12), 2025.\n \n\n\n\n
\n\n\n\n \n \n \"ModelingPaper\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{Nichols2025_modeling_approaches_for_estimating_the_effects,\n  author = {Emma Nichols and Ava Bindas and Samer Atshan and Howard Chang and Yao‐Yi Chiang and Birgit Claus Henn and Eleanor Hayes‐Larson and Kayleigh P Keller and Katrina L Kezios and Regina A Shih and Adam A Szpiro and Jordan Weiss and Sara D Adar and David M Knapp and Jinkook Lee and Jennifer Weuve},\n  howpublished = {Alzheimer's & Dementia 21 (12)},\n  title = {Modeling approaches for estimating the effects of risk factors using longitudinal lifecourse exposure data in dementia research},\n  url = {https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1002/alz.70971},\n  year = {2025}\n}\n\n
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\n \n\n \n \n \n \n \n \n FRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models.\n \n \n \n \n\n\n \n Pyo, J.; Jiao, Y.; Jung, D.; Li, Z.; Jang, L.; Kirsanova, S.; Kim, J.; Lin, Y.; Liu, Q.; Xie, J.; Askari, H.; Xu, N.; Chen, M.; and Chiang, Y.\n\n\n \n\n\n\n arXiv, 2025.\n \n\n\n\n
\n\n\n\n \n \n \"FRIEDA: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 \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{Pyo2025_frieda_benchmarking_multistep_cartographic_reasoning_in,\n  abstract = {Cartographic reasoning is the skill of interpreting geographic relationships by aligning legends, map scales, compass directions, map texts, and geometries across one or more map images. Although essential as a concrete cognitive capability and for critical tasks such as disaster response and urban planning, it remains largely unevaluated. Building on progress in chart and infographic understanding, recent large vision language model studies on map visual question-answering often treat maps as a special case of charts. In contrast, map VQA demands comprehension of layered symbology (e.g., symbols, geometries, and text labels) as well as spatial relations tied to orientation and distance that often span multiple maps and are not captured by chart-style evaluations. To address this gap, we introduce FRIEDA, a benchmark for testing complex open-ended cartographic reasoning in LVLMs. FRIEDA sources real map images from documents and reports in various domains and geographical areas. Following classifications in Geographic Information System (GIS) literature, FRIEDA targets all three categories of spatial relations: topological (border, equal, intersect, within), metric (distance), and directional (orientation). All questions require multi-step inference, and many require cross-map grounding and reasoning. We evaluate eleven state-of-the-art LVLMs under two settings: (1) the direct setting, where we provide the maps relevant to the question, and (2) the contextual setting, where the model may have to identify the maps relevant to the question before reasoning. Even the strongest models, Gemini-2.5-Pro and GPT-5-Think, achieve only 38.20% and 37.20% accuracy, respectively, far below human performance of 84.87%. These results reveal a persistent gap in multi-step cartographic reasoning, positioning FRIEDA as a rigorous benchmark to drive progress on spatial intelligence in LVLMs.},\n  author = {Jiyoon Pyo and Yuankun Jiao and Dongwon Jung and Zekun Li and Leeje Jang and Sofia Kirsanova and Jina Kim and Yijun Lin and Qin Liu and Junyi Xie and Hadi Askari and Nan Xu and Muhao Chen and Yao-Yi Chiang},\n  howpublished = {arXiv},\n  primaryclass = {cs.CV},\n  title = {FRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models},\n  url = {https://arxiv.org/api/cAn1zHHcKlclQ3NWAR92PR4QKLc},\n  year = {2025}\n}\n\n
\n
\n\n\n
\n Cartographic reasoning is the skill of interpreting geographic relationships by aligning legends, map scales, compass directions, map texts, and geometries across one or more map images. Although essential as a concrete cognitive capability and for critical tasks such as disaster response and urban planning, it remains largely unevaluated. Building on progress in chart and infographic understanding, recent large vision language model studies on map visual question-answering often treat maps as a special case of charts. In contrast, map VQA demands comprehension of layered symbology (e.g., symbols, geometries, and text labels) as well as spatial relations tied to orientation and distance that often span multiple maps and are not captured by chart-style evaluations. To address this gap, we introduce FRIEDA, a benchmark for testing complex open-ended cartographic reasoning in LVLMs. FRIEDA sources real map images from documents and reports in various domains and geographical areas. Following classifications in Geographic Information System (GIS) literature, FRIEDA targets all three categories of spatial relations: topological (border, equal, intersect, within), metric (distance), and directional (orientation). All questions require multi-step inference, and many require cross-map grounding and reasoning. We evaluate eleven state-of-the-art LVLMs under two settings: (1) the direct setting, where we provide the maps relevant to the question, and (2) the contextual setting, where the model may have to identify the maps relevant to the question before reasoning. Even the strongest models, Gemini-2.5-Pro and GPT-5-Think, achieve only 38.20% and 37.20% accuracy, respectively, far below human performance of 84.87%. These results reveal a persistent gap in multi-step cartographic reasoning, positioning FRIEDA as a rigorous benchmark to drive progress on spatial intelligence in LVLMs.\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, 2025.\n \n\n\n\n
\n\n\n\n \n \n \"WalkCLIP: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 \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{Xiang2025_walkclip_multimodal_learning_for_urban_walkability,\n  abstract = {Urban walkability is a cornerstone of public health, sustainability, and quality of life. Traditional walkability assessments rely on surveys and field audits, which are costly and difficult to scale. Recent studies have used satellite imagery, street view imagery, or population indicators to estimate walkability, but these single-source approaches capture only one dimension of the walking environment. Satellite data describe the built environment from above, but overlook the pedestrian perspective. Street view imagery captures conditions at the ground level, but lacks broader spatial context. Population dynamics reveal patterns of human activity but not the visual form of the environment. We introduce WalkCLIP, a multimodal framework that integrates these complementary viewpoints to predict urban walkability. WalkCLIP learns walkability-aware vision-language representations from GPT-4o generated image captions, refines these representations with a spatial aggregation module that incorporates neighborhood context, and fuses the resulting features with representations from a population dynamics foundation model. Evaluated at 4,660 locations throughout Minneapolis-Saint Paul, WalkCLIP outperforms unimodal and multimodal baselines in both predictive accuracy and spatial alignment. These results show that the integration of visual and behavioral signals yields reliable predictions of the walking environment.},\n  author = {Shilong Xiang and JangHyeon Lee and Min Namgung and Yao-Yi Chiang},\n  howpublished = {arXiv},\n  primaryclass = {cs.CV},\n  title = {WalkCLIP: Multimodal Learning for Urban Walkability Prediction},\n  url = {https://arxiv.org/api/4piXGGnxE2zcdD3UZMJB+nH1NA8},\n  year = {2025}\n}\n\n
\n
\n\n\n
\n Urban walkability is a cornerstone of public health, sustainability, and quality of life. Traditional walkability assessments rely on surveys and field audits, which are costly and difficult to scale. Recent studies have used satellite imagery, street view imagery, or population indicators to estimate walkability, but these single-source approaches capture only one dimension of the walking environment. Satellite data describe the built environment from above, but overlook the pedestrian perspective. Street view imagery captures conditions at the ground level, but lacks broader spatial context. Population dynamics reveal patterns of human activity but not the visual form of the environment. We introduce WalkCLIP, a multimodal framework that integrates these complementary viewpoints to predict urban walkability. WalkCLIP learns walkability-aware vision-language representations from GPT-4o generated image captions, refines these representations with a spatial aggregation module that incorporates neighborhood context, and fuses the resulting features with representations from a population dynamics foundation model. Evaluated at 4,660 locations throughout Minneapolis-Saint Paul, WalkCLIP outperforms unimodal and multimodal baselines in both predictive accuracy and spatial alignment. These results show that the integration of visual and behavioral signals yields reliable predictions of the walking environment.\n
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\n  \n 2024\n \n \n (6)\n \n \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 \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3637528.3671589,\n  address = {New York, NY, USA},\n  author = {Lin, Yijun and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},\n  doi = {10.1145/3637528.3671589},\n  isbn = {9798400704901},\n  keywords = {historical maps, synthetic map data, text detection and recognition, text spotting},\n  location = {Barcelona, Spain},\n  numpages = {11},\n  pages = {5387–5397},\n  publisher = {Association for Computing Machinery},\n  series = {KDD '24},\n  title = {Hyper-Local Deformable Transformers for Text Spotting on Historical Maps},\n  url = {https://doi.org/10.1145/3637528.3671589},\n  year = {2024}\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 \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3681769.3698577,\n  address = {New York, NY, USA},\n  author = {Olson, Rhett and Kim, Jina and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data},\n  doi = {10.1145/3681769.3698577},\n  isbn = {9798400711480},\n  keywords = {cartography, historical maps, minimum spanning tree, toponym},\n  location = {Atlanta, GA, USA},\n  numpages = {4},\n  pages = {9–12},\n  publisher = {Association for Computing Machinery},\n  series = {GeoSearch '24},\n  title = {Automatic Search of Multiword Place Names on Historical Maps},\n  url = {https://doi.org/10.1145/3681769.3698577},\n  year = {2024}\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 \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3681769.3698583,\n  address = {New York, NY, USA},\n  author = {Jang, Leeje and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data},\n  doi = {10.1145/3681769.3698583},\n  isbn = {9798400711480},\n  location = {Atlanta, GA, USA},\n  numpages = {7},\n  pages = {29–35},\n  publisher = {Association for Computing Machinery},\n  series = {GeoSearch '24},\n  title = {Enabling Semantic-Rich Location Search on Street View Imagery Using Multilingual POI Data},\n  url = {https://doi.org/10.1145/3681769.3698583},\n  year = {2024}\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 \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3681771.3699935,\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  address = {New York, NY, USA},\n  author = {Lee, JangHyeon and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge},\n  doi = {10.1145/3681771.3699935},\n  isbn = {9798400711503},\n  keywords = {Human mobility, Spatiotemporal, Transfer learning, Transformer},\n  location = {Atlanta, GA, USA},\n  numpages = {5},\n  pages = {55–59},\n  publisher = {Association for Computing Machinery},\n  series = {HuMob'24},\n  title = {CrossBag: A Bag of Tricks for Cross-City Mobility Prediction},\n  url = {https://doi.org/10.1145/3681771.3699935},\n  year = {2024}\n}\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 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 \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3687123.3698297,\n  address = {New York, NY, USA},\n  author = {Chen, Theresa and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},\n  doi = {10.1145/3687123.3698297},\n  isbn = {9798400711763},\n  keywords = {Multimodal machine learning, Spatial data, Species distribution modeling},\n  location = {Atlanta, GA, USA},\n  numpages = {11},\n  pages = {110–120},\n  publisher = {Association for Computing Machinery},\n  series = {GeoAI '24},\n  title = {MiTREE: Multi-input Transformer Ecoregion Encoder for Species Distribution Modelling},\n  url = {https://doi.org/10.1145/3687123.3698297},\n  year = {2024}\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
\n 
@inproceedings{10.1145/3687123.3698298,\n  address = {New York, NY, USA},\n  author = {Pyo, Jiyoon and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},\n  doi = {10.1145/3687123.3698298},\n  isbn = {9798400711763},\n  keywords = {Spatial entity linkage, entity matching, geospatial data},\n  location = {Atlanta, GA, USA},\n  numpages = {13},\n  pages = {86–98},\n  publisher = {Association for Computing Machinery},\n  series = {GeoAI '24},\n  title = {Leveraging Large Language Models for Generating Labeled Mineral Site Record Linkage Data},\n  url = {https://doi.org/10.1145/3687123.3698298},\n  year = {2024}\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 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 \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3589132.3625579,\n  address = {New York, NY, USA},\n  articleno = {35},\n  author = {Kim, Jina and Li, Zekun and Lin, Yijun and Namgung, Min and Jang, Leeje and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},\n  doi = {10.1145/3589132.3625579},\n  isbn = {9798400701689},\n  keywords = {linked data, text spotter, historical maps, automatic system},\n  location = {, Hamburg, Germany, },\n  numpages = {4},\n  publisher = {Association for Computing Machinery},\n  series = {SIGSPATIAL '23},\n  title = {The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps},\n  url = {https://doi.org/10.1145/3589132.3625579},\n  year = {2023}\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 \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3589132.3625648,\n  address = {New York, NY, USA},\n  articleno = {98},\n  author = {Lin, Yijun and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},\n  doi = {10.1145/3589132.3625648},\n  isbn = {9798400701689},\n  keywords = {physics-guided machine learning, spatiotemporal predictive learning, spatial AI},\n  location = {Hamburg, Germany},\n  numpages = {11},\n  publisher = {Association for Computing Machinery},\n  series = {SIGSPATIAL '23},\n  title = {Modeling Spatially Varying Physical Dynamics for Spatiotemporal Predictive Learning},\n  url = {https://doi.org/10.1145/3589132.3625648},\n  year = {2023}\n}\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 \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3589132.3628368,\n  address = {New York, NY, USA},\n  articleno = {7},\n  author = {Olson, Rhett M and Kim, Jina and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},\n  doi = {10.1145/3589132.3628368},\n  isbn = {9798400701689},\n  keywords = {temporal analysis, toponym, cartography, historical maps},\n  location = {<conf-loc>, <city>Hamburg</city>, <country>Germany</country>, </conf-loc>},\n  numpages = {2},\n  publisher = {Association for Computing Machinery},\n  series = {SIGSPATIAL '23},\n  title = {An Automatic Approach to Finding Geographic Name Changes on Historical Maps},\n  url = {https://doi.org/10.1145/3589132.3628368},\n  year = {2023}\n}\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 \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3589132.3628372,\n  address = {New York, NY, USA},\n  articleno = {11},\n  author = {Kim, Jina and Chiang, Yao-Yi},\n  booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},\n  doi = {10.1145/3589132.3628372},\n  isbn = {9798400701689},\n  keywords = {representation learning, online text, spatial triad},\n  location = {<conf-loc>, <city>Hamburg</city>, <country>Germany</country>, </conf-loc>},\n  numpages = {2},\n  publisher = {Association for Computing Machinery},\n  series = {SIGSPATIAL '23},\n  title = {Towards Learning of Spatial Triad from Online Text},\n  url = {https://doi.org/10.1145/3589132.3628372},\n  year = {2023}\n}\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
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@article{avelar2023water,\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  number = {1},\n  pages = {1--19},\n  publisher = {BioMed Central},\n  title = {Water, Sanitation, and Hygiene (WaSH) insecurity in unhoused communities of Los Angeles, California},\n  volume = {22},\n  year = {2023}\n}\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  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  publisher = {CRC Press},\n  title = {GeoAI for the Digitization of Historical Maps},\n  year = {2023}\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 \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{li-etal-2023-geolm,\n  address = {Singapore},\n  author = {Li, Zekun  and\nZhou, Wenxuan  and\nChiang, Yao-Yi  and\nChen, Muhao},\n  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},\n  doi = {10.18653/v1/2023.emnlp-main.317},\n  editor = {Bouamor, Houda  and\nPino, Juan  and\nBali, Kalika},\n  month = {December},\n  pages = {5227--5240},\n  publisher = {Association for Computational Linguistics},\n  title = {{G}eo{LM}: Empowering Language Models for Geospatially Grounded Language Understanding},\n  url = {https://aclanthology.org/2023.emnlp-main.317},\n  year = {2023}\n}\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  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  publisher = {IEEE},\n  title = {The Best Protection Is Attack: Fooling Scene Text Recognition with Minimal Pixels},\n  year = {2023}\n}\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
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@article{zhang2023optimal,\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  pages = {120442},\n  publisher = {Elsevier},\n  title = {Optimal and efficient planning of charging stations for electric vehicles in urban areas: formulation, complexity and solutions},\n  volume = {230},\n  year = {2023}\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
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@article{9924526,\n  author = {Zhang, Ying and Li, Muyang and Chen, Yuanchang and Chiang, Yao-Yi and Hua, Yunpeng},\n  doi = {10.1109/TSG.2022.3214680},\n  journal = {IEEE Transactions on Smart Grid},\n  pages = {1-1},\n  title = {A Constraint-based Routing and Charging Methodology for Battery Electric Vehicles with Deep Reinforcement Learning},\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
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@article{dai2022ace,\n  author = {Dai, Pengwen and Yao, Siyuan and Li, Zekun and Zhang, Sanyi and Cao, Xiaochun},\n  journal = {IEEE Transactions on Image Processing},\n  pages = {4076--4089},\n  publisher = {IEEE},\n  title = {ACE: Anchor-free corner evolution for real-time arbitrarily-oriented object detection},\n  volume = {31},\n  year = {2022}\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,\n  abstract = {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  author = {Gao, Yuan and Chiang, Yao-Yi and Zhang, Xiaoxi and Zhang, Min},\n  doi = {https://doi.org/10.1111/tgis.12975},\n  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/tgis.12975},\n  journal = {Transactions in GIS},\n  number = {6},\n  pages = {2415-2439},\n  title = {Traffic volume prediction for scenic spots based on multi-source and heterogeneous data},\n  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12975},\n  volume = {26},\n  year = {2022}\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\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
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@inproceedings{hu2022clustering,\n  author = {Hu, Haoji and Lin, Haowen and Chiang, Yao-Yi},\n  booktitle = {2022 IEEE International Conference on Big Data (Big Data)},\n  organization = {IEEE},\n  pages = {575--584},\n  title = {Clustering Human Mobility with Multiple Spaces},\n  year = {2022}\n}\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
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@article{li2022spabert,\n  author = {Li, Zekun and Kim, Jina and Chiang, Yao-Yi and Chen, Muhao},\n  journal = {arXiv preprint arXiv:2210.12213},\n  title = {SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation},\n  year = {2022}\n}\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  author = {Lin, Yijun and Chiang, Yao-Yi},\n  booktitle = {2022 IEEE International Conference on Big Data (Big Data)},\n  organization = {IEEE},\n  pages = {1024--1033},\n  title = {A Semi-Supervised Learning Approach for Abnormal Event Prediction on Large Network Operation Time-Series Data},\n  year = {2022}\n}\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  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  organization = {IEEE},\n  pages = {4782--4787},\n  title = {Cell-by-Cell Line-of-Sight Probability Models Based on Real-World Base Station Deployment},\n  year = {2022}\n}\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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@inproceedings{namgung2022incorporating,\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  title = {Incorporating spatial context for post-OCR in map images},\n  year = {2022}\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  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  publisher = {IOS Press},\n  title = {Building Spatio-Temporal Knowledge Graphs from Vectorized Topographic Historical Maps},\n  urllink = {https://content.iospress.com/articles/semantic-web/sw222918},\n  year = {2022}\n}\n\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 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  author = {Avelar Portillo, L. J. and Chiang, Y.-Y. and Franklin, M. and Ko, C. and Vasquez, A. },\n  booktitle = {{American Association of Geographers Annual Meeting}},\n  title = {{Impacts of COVID-19 on Water, Sanitation, and Hygiene (WaSH) Access in Skid Row, Los Angeles}},\n  year = {2021}\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  author = {Avelar Portillo, L. J. and Park, L. and Ko, C. and Vasquez, A. and Franklin, M. and Chiang, Y.-Y. },\n  booktitle = {{{Proceedings of the 30th International Cartographic Conference\n(ICC'21)}}},\n  title = {{Measuring Closest Water, Sanitation, and Hygiene Facilities in Unhoused Communities of Los Angeles}},\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 26 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  address = {New York, NY, USA},\n  author = {Gil, Yolanda and Garijo, Daniel and Khider, Deborah and\nKnoblock, Craig A and Ratnakar, Varun and Osorio, Maximiliano\nand Vargas, Hern{\\'a}n and Pham, Minh and Pujara, Jay and\nShbita, Basel and Vu, Binh and Chiang, Yao-Yi and Feldman, Dan\nand Lin, Yijun and Song, Hayley and Kumar, Vipin and Khandelwal,\nAnkush and Steinbach, Michael and Tayal, Kshitij and Xu,\nShaoming and Pierce, Suzanne A and Pearson, Lissa and\nHardesty-Lewis, Daniel and Deelman, Ewa and Silva, Rafael\nFerreira Da and Mayani, Rajiv and Kemanian, Armen R and Shi,\nYuning and Leonard, Lorne and Peckham, Scott and Stoica, Maria\nand Cobourn, Kelly and Zhang, Zeya and Duffy, Christopher and\nShu, Lele},\n  doi = {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  issn = {2160-6455},\n  journal = {ACM Trans. Interact. Intell. Syst.},\n  month = {July},\n  number = {2},\n  pages = {1--49},\n  publisher = {Association for Computing Machinery},\n  title = {{Artificial Intelligence for Modeling Complex Systems: Taming the\nComplexity of Expert Models to Improve Decision Making}},\n  url = {https://doi.org/10.1145/3453172},\n  volume = {11},\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
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@inproceedings{li2021synthetic,\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  title = {Synthetic map generation to provide unlimited training data for historical map text detection},\n  year = {2021}\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  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  title = {Chartocr: Data extraction from charts images via a deep hybrid framework},\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  author = {Park, L. and Birdwell, A. and Chiang, Y.-Y. },\n  booktitle = {{{Proceedings of the 30th International Cartographic Conference\n(ICC'21)}}},\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  year = {2021}\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,\n  abstract = {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  article-number = {5801},\n  author = {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.},\n  doi = {10.3390/s21175801},\n  issn = {1424-8220},\n  journal = {Sensors},\n  number = {17},\n  pubmedid = {34502692},\n  title = {W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets},\n  url = {https://www.mdpi.com/1424-8220/21/17/5801},\n  volume = {21},\n  year = {2021}\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 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  author = {Uhl, Johannes H and Leyk, Stefan and Li, Zekun and Duan, Weiwei\nand Shbita, Basel and Chiang, Yao-Yi and Knoblock, Craig A},\n  doi = {10.3390/rs13183672},\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  journal = {Remote Sensing},\n  language = {en},\n  month = {September},\n  number = {18},\n  pages = {3672},\n  publisher = {Multidisciplinary Digital Publishing Institute},\n  title = {{Combining Remote-Sensing-Derived Data and Historical Maps for\nLong-Term Back-Casting of Urban Extents}},\n  url = {https://www.mdpi.com/2072-4292/13/18/3672},\n  volume = {13},\n  year = {2021}\n}\n\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  author = {Yue, Mingxuan and Chiang, Yao-Yi and Shahabi, Cyrus},\n  booktitle = {{Machine Learning and Knowledge Discovery in Databases. Applied\nData Science Track}},\n  doi = {10.1007/978-3-030-86514-6_28},\n  pages = {453--469},\n  publisher = {Springer International Publishing},\n  title = {{VAMBC: A Variational Approach for Mobility Behavior Clustering}},\n  url = {http://dx.doi.org/10.1007/978-3-030-86514-6_28},\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  author = {Zhang, Ying and Wu, Bin and Chiang, Yao-Yi and Zhang, Xin and\nChen, Yuanchang and Li, Muyang and Li, Fanyu},\n  doi = {10.1016/j.engappai.2021.104378},\n  issn = {0952-1976},\n  journal = {Engineering applications of artificial intelligence},\n  month = {September},\n  pages = {104378},\n  title = {{BiS4EV: A fast routing algorithm considering charging stations and\npreferences for electric vehicles}},\n  url = {https://www.sciencedirect.com/science/article/pii/S0952197621002268},\n  volume = {104},\n  year = {2021}\n}\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 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  author = {Chiang, Yao-Yi and Duan, Weiwei and Leyk, Stefan and Uhl,\nJohannes H and Knoblock, Craig A},\n  doi = {10.1007/978-3-319-66908-3},\n  isbn = {9783319669076},\n  publisher = {Springer International Publishing},\n  series = {SpringerBriefs in Geography},\n  title = {{Using Historical Maps in Scientific Studies: Applications,\nChallenges, and Best Practices}},\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  year = {2020}\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  author = {Chiang, Yao-Yi and Lin, Yijun},\n  booktitle = {{The Geographic Information Science \\& Technology Body of\nKnowledge (4th Quarter 2020 Edition)}},\n  doi = {10.22224/gistbok/2020.4.2},\n  editor = {Wilson, John P},\n  publisher = {The University Consortium for Geographic Information Science\n(UCGIS)},\n  title = {{Design, Development, Testing, and Deployment of GIS Applications}},\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  year = {2020}\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  author = {Duan, Weiwei and Chiang, Yao-Yi and Leyk, Stefan and Uhl,\nJohannes H and Knoblock, Craig A},\n  doi = {10.1080/13658816.2019.1698742},\n  issn = {1365-8816},\n  journal = {International journal of geographical information science: IJGIS},\n  month = {April},\n  number = {4},\n  pages = {824--849},\n  publisher = {Taylor \\& Francis},\n  title = {{Automatic alignment of contemporary vector data and\ngeoreferenced historical maps using reinforcement learning}},\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  volume = {34},\n  year = {2020}\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  author = {Karroum, Khaoula and Lin, Yijun and Chiang, Yao-Yi and Ben Maissa,\nYann and El Haziti, Mohamed and Sokolov, Anton and Delbarre,\nHerv{\\'e}},\n  doi = {10.1007/s12647-020-00371-8},\n  issn = {0974-9853},\n  journal = {MAPAN},\n  month = {March},\n  title = {{A Review of Air Quality Modeling}},\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  year = {2020}\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
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@article{Li2020-my,\n  author = {Li, Z and Chiang, Y Y and Tavakkol, S and Shbita, B and Uhl, J H\nand {others}},\n  journal = {Proceedings of ACM Knowledge Discovery and Data Mining\nConference (KDD)},\n  publisher = {dl.acm.org},\n  title = {{An Automatic Approach for Generating Rich, Linked Geo-Metadata\nfrom Historical Map Images}},\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  year = {2020}\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  author = {Lin, Y and Chiang, Y-Y and Franklin, M and Eckel, P S and\nAmbite, J L},\n  booktitle = {{Proceedings of IEEE International Conference on Data Mining\n(ICDM)}},\n  title = {{Building Autocorrelation-Aware Representations for Fine-Scale\nSpatiotemporal Prediction}},\n  url-file = {papers/Lin-et-al.-2020-Building-Autocorrelation-Aware-Representations-for-Fine-Scale-Spatiotemporal-Prediction.pdf},\n  year = {2020}\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  author = {Shbita, Basel and Knoblock, Craig A and Duan, Weiwei and Chiang,\nYao-Yi and Uhl, Johannes H and Leyk, Stefan},\n  booktitle = {{The Semantic Web}},\n  doi = {10.1007/978-3-030-49461-2\\_24},\n  pages = {409--426},\n  publisher = {Springer International Publishing},\n  title = {{Building Linked Spatio-Temporal Data from Vectorized Historical\nMaps}},\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  year = {2020}\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  author = {Zhang, Y and Wang, Y and Li, F and Wu, B and Chiang, Y Y and\nZhang, X},\n  doi = {10.1109/TITS.2020.2990694},\n  issn = {1558-0016},\n  journal = {IEEE Transactions on Intelligent Transportation Systems},\n  pages = {1--6},\n  title = {{Efficient Deployment of Electric Vehicle Charging Infrastructure:\nSimultaneous Optimization of Charging Station Placement and\nCharging Pile Assignment}},\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  year = {2020}\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 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  address = {New York},\n  author = {Anastasiou, Chrysovalantis and Lin, Jianfa and He, Chaoyang and\nChiang, Yao-Yi and Shahabi, Cyrus},\n  booktitle = {{Proceedings of the 2nd ACM SIGSPATIAL workshop ARIC'19}},\n  title = {{ADMSv2: A Modern Architecture for Transportation Data Management\nand Analysis}},\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  year = {2019}\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  address = {New York, NY, USA},\n  author = {Cheng, Yuanbin and Chiang, Yao-Yi},\n  doi = {10.1145/3307599.3307602},\n  journal = {SIGSPATIAL Special},\n  month = {January},\n  number = {3},\n  pages = {4--5},\n  publisher = {Association for Computing Machinery},\n  title = {{Automatic intersection extraction and building arrangement with\nStarCraft II maps}},\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  volume = {10},\n  year = {2019}\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
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@inproceedings{Chiang2019-yx,\n  author = {Chiang, Y Y and Lin, Y and Franklin, M and Eckel, S P and\nAmbite, J L and Ku, W},\n  booktitle = {{Proceedings of the 2019 IEEE First International Conference on\nCognitive Machine Intelligence (CogMI)}},\n  doi = {10.1109/CogMI48466.2019.00037},\n  month = {December},\n  pages = {202--209},\n  title = {{Building Explainable Predictive Analytics for Location-Dependent\nTime-Series Data}},\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  year = {2019}\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  author = {Gao, Y and Duan, Z and Shi, W and Feng, J and Chiang, Y-Y},\n  booktitle = {{Proceedings of the 2019 6th International Conference on\nBehavioral, Economic and Socio-Cultural Computing (BESC)}},\n  doi = {10.1109/BESC48373.2019.8963449},\n  month = {October},\n  pages = {1--6},\n  title = {{Personalized Recommendation Method of POI Based on Deep Neural\nNetwork}},\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  year = {2019}\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  address = {New York, NY, USA},\n  author = {Garijo, Daniel and Khider, Deborah and Ratnakar, Varun and Gil,\nYolanda and Deelman, Ewa and da Silva, Rafael Ferreira and\nKnoblock, Craig and Chiang, Yao-Yi and Pham, Minh and Pujara,\nJay and Vu, Binh and Feldman, Dan and Mayani, Rajiv and Cobourn,\nKelly and Duffy, Chris and Kemanian, Armen and Shu, Lele and\nKumar, Vipin and Khandelwal, Ankush and Tayal, Kshitij and\nPeckham, Scott and Stoica, Maria and Dabrowski, Anna and\nHardesty-Lewis, Daniel and Pierce, Suzanne},\n  booktitle = {{Proceedings of the 24th International Conference on Intelligent\nUser Interfaces: Companion}},\n  doi = {10.1145/3308557.3308711},\n  isbn = {9781450366731},\n  location = {Marina del Ray, California},\n  month = {March},\n  pages = {111--112},\n  publisher = {Association for Computing Machinery},\n  series = {IUI '19},\n  title = {{An intelligent interface for integrating climate, hydrology,\nagriculture, and socioeconomic models}},\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  year = {2019}\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
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@misc{Khider2019-zm,\n  author = {Khider, D and Gil, Y and Cobourn, K M and Deelman, E and\nDuffy, C and Ferreira da Silva, R and Kemanian, A and\nKnoblock, C and Kumar, V and Peckham, S D and Chiang, Y Y and\nFeldman, D and Garijo, D and Hardesty Lewis, D and\nKhandelwal, A and Mayani, R and Osorio, M and Pahm, M and\nPierce, S A and Pujara, J and Ratnakar, V and Shu, L and\nSong, H J and Shbita, B and Stoica, M and Vu, B and Pearson,\nL},\n  howpublished = {AGU Fall Meeting Abstracts},\n  month = {December},\n  title = {{MINT: An intelligent interface for understanding the impacts\nof climate change on hydrological, agricultural and economic\nsystems}},\n  url = {https://ui.adsabs.harvard.edu/abs/2019AGUFMPA33C1108K},\n  year = {2019}\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  author = {Li, Kenan and Habre, Rima and Deng, Huiyu and Urman, Robert and\nMorrison, John and Gilliland, Frank D and Ambite, Jos{\\'e} Luis\nand Stripelis, Dimitris and Chiang, Yao-Yi and Lin, Yijun and\nBui, Alex At and King, Christine and Hosseini, Anahita and Van\nVliet, Eleanne and Sarrafzadeh, Majid and Eckel, Sandrah P},\n  doi = {10.2196/11201},\n  issn = {2291-5222},\n  journal = {JMIR mHealth and uHealth},\n  language = {en},\n  month = {February},\n  number = {2},\n  pages = {e11201},\n  pmc = {PMC6386646},\n  pmid = {30730297},\n  title = {{Applying Multivariate Segmentation Methods to Human Activity\nRecognition From Wearable Sensors' Data}},\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  volume = {7},\n  year = {2019}\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  author = {Shbita, Basel and Vu, Binh and Feldman, Dan and Pham, Minh and\nRajendran, A and Knoblock, C A and Pujara, J and Chiang, Y-Y},\n  booktitle = {{Proceedings of the Workshop on Advanced Knowledge Technologies\nfor Science in a FAIR World (AKTS) 2019}},\n  month = {September},\n  title = {{Creating a FAIR Data Catalog to Support Scientific Modeling}},\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  year = {2019}\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  address = {New York, NY, USA},\n  author = {Tavakkol, Sasan and Chiang, Yao-Yi and Waters, Tim and Han, Feng\nand Prasad, Kisalaya and Kiveris, Raimondas},\n  booktitle = {{Proceedings of the 3rd ACM SIGSPATIAL International Workshop on\nAI for Geographic Knowledge Discovery}},\n  doi = {10.1145/3356471.3365236},\n  isbn = {9781450369572},\n  location = {Chicago, IL, USA},\n  month = {November},\n  pages = {48--51},\n  publisher = {Association for Computing Machinery},\n  series = {GeoAI 2019},\n  title = {{Kartta Labs: Unrendering Historical Maps}},\n  url = {https://doi.org/10.1145/3356471.3365236},\n  url-file = {papers/Tavakkol-et-al.-2019-Kartta-Labs-Unrendering-Historical-Maps.pdf},\n  year = {2019}\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
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@article{Wu2019-qe,\n  author = {Wu, J and Wei, P and Yuan, X and Shu, Z and Chiang, Y Y and Fu, Z\nand Deng, M},\n  doi = {10.1109/ACCESS.2019.2907114},\n  issn = {2169-3536},\n  journal = {IEEE Access},\n  pages = {40649--40662},\n  title = {{A New Gabor Filter-Based Method for Automatic Recognition of\nHatched Residential Areas}},\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  volume = {7},\n  year = {2019}\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  author = {Yue, M and Li, Y and Yang, H and Ahuja, R and Chiang, Y and\nShahabi, C},\n  booktitle = {{Proceedings of the 2019 IEEE International Conference on Big\nData (Big Data)}},\n  doi = {10.1109/BigData47090.2019.9006561},\n  month = {December},\n  pages = {988--997},\n  title = {{DETECT: Deep Trajectory Clustering for Mobility-Behavior\nAnalysis}},\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  year = {2019}\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  author = {Zhang, J and Shen, T and Wang, W and Jiang, X and Ku, W and Sun,\nM and Chiang, Y Y},\n  booktitle = {{Proceedings of the 2019 20th IEEE International Conference on\nMobile Data Management (MDM)}},\n  doi = {10.1109/MDM.2019.00-56},\n  issn = {2375-0324},\n  month = {June},\n  pages = {201--209},\n  title = {{A VLOS Compliance Solution to Ground/Aerial Parcel Delivery\nProblem}},\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  year = {2019}\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  author = {Zhang, Ying and Ma, Qunfei and Chiang, Yao-Yi and Knoblock, Craig\nand Zhang, Xin and Yang, Puhai and Gao, Minghe and Hu, Xiang},\n  doi = {10.1016/j.knosys.2019.02.031},\n  issn = {0950-7051},\n  journal = {Knowledge-Based Systems},\n  month = {June},\n  pages = {57--72},\n  title = {{Extracting geographic features from the Internet: A geographic\ninformation mining framework}},\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  volume = {174},\n  year = {2019}\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  author = {Chiang, Y Y and Feldman, D},\n  howpublished = {The 1st workshop of the NSF project: SI2-S2I2\nConceptualization: Geospatial Software Institute (GSI)},\n  title = {{Next Generation Framework for Imagery Recognition and\nAnalysis}},\n  url-file = {papers/Chiang-and-Feldman-2018-Next-Generation-Framework-for-Imagery-Recognition-and-Analysis.pdf},\n  year = {2018}\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  author = {Duan, Weiwei and Chiang, Y and Knoblock, Crang A and Leyk,\nStefan and Uhl, J},\n  booktitle = {{Proceedings of the AutoCarto}},\n  pages = {59--63},\n  title = {{Automatic generation of precisely delineated geographic features\nfrom georeferenced historical maps using deep learning}},\n  url-file = {papers/Duan-et-al.-2018-Automatic-generation-of-precisely-delineated-geographic-features-from-georeferenced-historical-maps-using-deep-learning.pdf},\n  year = {2018}\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  address = {New York, NY, USA},\n  author = {Duan, Weiwei and Chiang, Yao-Yi},\n  doi = {10.1145/3178392.3178396},\n  journal = {SIGSPATIAL Special},\n  month = {January},\n  number = {3},\n  pages = {6--7},\n  publisher = {Association for Computing Machinery},\n  title = {{SRC: a fully automatic geographic feature recognition system}},\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  volume = {9},\n  year = {2018}\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  author = {Gil, Yolanda and Cobourn, Kelly and Deelman, Ewa and Duffy,\nChris and da Silva, Rafael Ferreira and Kemanian, Armen and\nKnoblock, Craig and Kumar, Vipin and Peckham, Scott and\nCarvalho, Lucas and Chiang, Yao-Yi and Garijo, Daniel and\nKhider, Deborah and Khandelwal, Ankush and Pahm, Minh and\nPujara, Jay and Ratnakar, Varun and Stoica, Maria and Vu, Binh},\n  booktitle = {{Proceedings of the 9th International Congress on Environmental\nModelling and Software}},\n  title = {{MINT: Model Integration Through Knowledge-Powered Data and\nProcess Composition}},\n  url-file = {papers/Gil-et-al.-2018-MINT-Model-Integration-Through-Knowledge-Powered-Data-and-Process-Composition.pdf},\n  year = {2018}\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  author = {Lin, Chun and Su, Hang and Knoblock, Craig A and Chiang, Yao-Yi\nand Duan, Weiwei and Leyk, Stefan and Uhl, Johannes H},\n  booktitle = {{Proceedings of the SemSci 2018: Enabling Open Semantic Science}},\n  pages = {59--67},\n  title = {{Building Linked Data from Historical Maps}},\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  year = {2018}\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  address = {New York, NY, USA},\n  author = {Lin, Haowen and Chiang, Yao-Yi},\n  booktitle = {{Proceedings of the 7th ACM SIGSPATIAL International Workshop on\nAnalytics for Big Geospatial Data}},\n  doi = {10.1145/3282834.3282842},\n  isbn = {9781450360418},\n  location = {Seattle, WA, USA},\n  month = {November},\n  pages = {20--27},\n  publisher = {Association for Computing Machinery},\n  series = {BigSpatial 2018},\n  title = {{An Uncertainty Aware Method for Geographic Data Conflation}},\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  year = {2018}\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  address = {New York, NY, USA},\n  author = {Lin, Haowen and Chiang, Yao-Yi},\n  doi = {10.1145/3178392.3178400},\n  journal = {SIGSPATIAL Special},\n  month = {January},\n  number = {3},\n  pages = {14--15},\n  publisher = {Association for Computing Machinery},\n  title = {{SRC: automatic extraction of phrase-level map labels from\nhistorical maps}},\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  volume = {9},\n  year = {2018}\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  address = {New York, NY, USA},\n  author = {Lin, Yijun and Mago, Nikhit and Gao, Yu and Li, Yaguang and\nChiang, Yao-Yi and Shahabi, Cyrus and Ambite, Jos{\\'e} Luis},\n  booktitle = {{Proceedings of the 26th ACM SIGSPATIAL International Conference\non Advances in Geographic Information Systems}},\n  doi = {10.1145/3274895.3274907},\n  isbn = {9781450358897},\n  location = {Seattle, Washington},\n  month = {November},\n  pages = {359--368},\n  publisher = {Association for Computing Machinery},\n  series = {SIGSPATIAL '18},\n  title = {{Exploiting spatiotemporal patterns for accurate air quality\nforecasting using deep learning}},\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  year = {2018}\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  address = {New York, NY, USA},\n  author = {Nguyen, Kien and Yang, Jingyun and Lin, Yijun and Lin, Jianfa\nand Chiang, Yao-Yi and Shahabi, Cyrus},\n  booktitle = {{Proceedings of the 26th ACM SIGSPATIAL International Conference\non Advances in Geographic Information Systems}},\n  doi = {10.1145/3274895.3274911},\n  isbn = {9781450358897},\n  location = {Seattle, Washington},\n  month = {November},\n  pages = {560--563},\n  publisher = {Association for Computing Machinery},\n  series = {SIGSPATIAL '18},\n  title = {{Los angeles metro bus data analysis using GPS trajectory and\nschedule data (demo paper)}},\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  year = {2018}\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  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\nWeiwei and Knoblock, Craig A},\n  booktitle = {{Proceedings of the UCGIS/AutoCarto}},\n  pages = {123 -- 124},\n  title = {{Exploring the Potential of Deep Learning for Settlement Symbol\nExtraction from Historical Map Documents}},\n  url-file = {papers/Uhl-et-al.-2018-Exploring-the-Potential-of-Deep-Learning-for-Settlement-Symbol-Extraction-from-Historical-Map-Documents.pdf},\n  year = {2018}\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  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\nWeiwei and Knoblock, Craig A},\n  doi = {10.1049/iet-ipr.2018.5484},\n  issn = {1751-9667, 1751-9667},\n  journal = {IET Image Processing},\n  month = {July},\n  number = {11},\n  pages = {2084--2091},\n  publisher = {IET Digital Library},\n  title = {{Spatialising uncertainty in image segmentation using weakly\nsupervised convolutional neural networks: a case study from\nhistorical map processing}},\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  volume = {12},\n  year = {2018}\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
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@article{Uhl2018-tj,\n  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\nWeiwei and Knoblock, Craig A},\n  doi = {10.3390/ijgi7040148},\n  issn = {2220-9964},\n  journal = {ISPRS international journal of geo-information},\n  language = {en},\n  month = {April},\n  number = {4},\n  pages = {148},\n  pmc = {PMC6500493},\n  pmid = {31061817},\n  publisher = {Multidisciplinary Digital Publishing Institute},\n  title = {{Map archive mining: visual-analytical approaches to explore\nlarge historical map collections}},\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  volume = {7},\n  year = {2018}\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
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@article{VoPham2018-zv,\n  author = {VoPham, Trang and Hart, Jaime E and Laden, Francine and Chiang,\nYao-Yi},\n  doi = {10.1186/s12940-018-0386-x},\n  issn = {1476-069X},\n  journal = {Environmental health: a global access science source},\n  language = {en},\n  month = {April},\n  number = {1},\n  pages = {40},\n  pmc = {PMC5905121},\n  pmid = {29665858},\n  title = {{Emerging trends in geospatial artificial intelligence (geoAI):\npotential applications for environmental epidemiology}},\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  volume = {17},\n  year = {2018}\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  author = {Wu, J and Wan, Y and Chiang, Y Y and Fu, Z and Deng, M},\n  doi = {10.1109/ACCESS.2018.2793302},\n  issn = {2169-3536},\n  journal = {IEEE Access},\n  pages = {4904--4915},\n  title = {{A Matching Algorithm Based on Voronoi Diagram for Multi-Scale\nPolygonal Residential Areas}},\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  volume = {6},\n  year = {2018}\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
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@inproceedings{Yu2018-tj,\n  author = {Yu, X and Cheng, Y and Lin, Y and Chiang, Y Y and Stripelis, D\nand Ambite, J L},\n  booktitle = {{Proceedings of the AutoCarto}},\n  pages = {135 -- 145},\n  title = {{MAPINS: An Intra-City PM2.5 Modeling Web Application Using A\nScalable Data Management and Analysis System Integrating Public\nMulti-Source Data}},\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  year = {2018}\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
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@inproceedings{Chiang2017-qn,\n  author = {Chiang, Y Y and Jain, A and Bandyopadhyay, B and Knoblock, A C},\n  booktitle = {{Proceedings of the Symposium on Solid and Physical Modeling\n(SPM)}},\n  title = {{Automatic Learning of User Design Rationales from Examples}},\n  url-file = {papers/Chiang-et-al.-2017-Automatic-Learning-of-User-Design-Rationales-from-Examples.pdf},\n  year = {2017}\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
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@inproceedings{Chiang2017-vb,\n  author = {Chiang, Yao-Yi},\n  booktitle = {{Proceedings of the 28th International Cartographic Conference}},\n  title = {{{LINKING HISTORICAL MAPS TO USC SHOAH FOUNDATION VISUAL HISTORY\nARCHIVE }}},\n  year = {2017}\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
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@incollection{Chiang2017-ym,\n  author = {Chiang, Yao-Yi},\n  booktitle = {{Recent Trends in Image Processing and Pattern Recognition}},\n  editor = {Santosh, K C and Hangarge, Mallikarjun and Bevilacqua,\nVitoantonio and Negi, Atul},\n  isbn = {9789811048593},\n  pages = {111--124},\n  publisher = {Springer Singapore},\n  title = {{Unlocking Textual Content from Historical Maps - Potentials and\nApplications, Trends, and Outlooks}},\n  url-file = {papers/Chiang-2017-Unlocking-Textual-Content-from-Historical-Maps-Potentials-and-Applications,-Trends,-and-Outlooks.pdf},\n  volume = {709},\n  year = {2017}\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
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@inproceedings{Duan2017-de,\n  address = {New York, NY, USA},\n  author = {Duan, Weiwei and Chiang, Yao-Yi and Knoblock, Craig A and Jain,\nVinil and Feldman, Dan and Uhl, Johannes H and Leyk, Stefan},\n  booktitle = {{Proceedings of the 1st Workshop on Artificial Intelligence and\nDeep Learning for Geographic Knowledge Discovery}},\n  doi = {10.1145/3149808.3149816},\n  isbn = {9781450354981},\n  location = {Los Angeles, California},\n  month = {November},\n  pages = {45--54},\n  publisher = {Association for Computing Machinery},\n  series = {GeoAI '17},\n  title = {{Automatic alignment of geographic features in contemporary\nvector data and historical maps}},\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  year = {2017}\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
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@misc{Holmes-Wong2017-ti,\n  author = {Holmes-Wong, D and Chiang, Y Y},\n  howpublished = {Digital Library Federation (DLF) Forum},\n  location = {Pittsburg, PA, USA},\n  title = {{Unlocking Maps for Discovery and Other Purpose}},\n  year = {2017}\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
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@inproceedings{Leyk2017-zc,\n  author = {Leyk, Stefan and Chiang, Yao-Yi},\n  booktitle = {{Proceedings of the 28th International Cartographic Conference}},\n  title = {{Implementing the Concept of Geographic Context for Efficient\nRecognition from Large-Scale Topographic Map Series}},\n  year = {2017}\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  address = {New York, NY, USA},\n  author = {Lin, Yijun and Chiang, Yao-Yi and Pan, Fan and Stripelis,\nDimitrios and Ambite, Jose Luis and Eckel, Sandrah P and Habre,\nRima},\n  booktitle = {{Proceedings of the 25th ACM SIGSPATIAL International Conference\non Advances in Geographic Information Systems}},\n  conference = {SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on\nAdvances in Geographic Information Systems},\n  doi = {10.1145/3139958.3140013},\n  isbn = {9781450354905},\n  language = {en},\n  location = {Redondo Beach CA USA},\n  month = {November},\n  pages = {1--10},\n  pmc = {PMC5841919},\n  pmid = {29527599},\n  publisher = {ACM},\n  title = {{Mining Public Datasets for Modeling Intra-City PM2.5\nConcentrations at a Fine Spatial Resolution}},\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  volume = {8},\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  author = {Nanetti, A and Cattaneo, A and Cheong, S A and Chiang, Y Y and\nLin, C Y},\n  booktitle = {{Proceedings of the ICA Pre-Conference Workshop on Mapping Tools\nfor Non-Mapping Experts: Incorporating Geospatial Visualization\nTools in Libraries}},\n  title = {{Visual Knowledge Aggregation: From Static to Dynamic Information\nSystems in Library Contexts}},\n  year = {2017}\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  author = {Stripelis, D and Ambite, J L and Chiang, Y Y and Eckel, S P and\nHabre, R},\n  booktitle = {{Proceedings of the 2017 IEEE 33rd International Conference on\nData Engineering (ICDE)}},\n  doi = {10.1109/ICDE.2017.198},\n  pages = {1407--1408},\n  title = {{{A Scalable Data Integration and Analysis Architecture for\nSensor Data of Pediatric Asthma}}},\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  year = {2017}\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  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\nWeiwei and Knoblock, Craig A},\n  howpublished = {The International Land Use Symposium 2017 (Spatial data\nmodelling and visualisation to enlighten sustainable policy\nmaking)},\n  location = {Dresden, Germany},\n  title = {{Machine-learning based Approaches for Extracting Settlement\nFeatures from Historical Maps}},\n  year = {2017}\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  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,\nWeiwei and Knoblock, Craig A},\n  booktitle = {{Proceedings of the 8th ​​International Conference of Pattern\nRecognition Systems (ICPRS 2017)}},\n  doi = {10.1049/cp.2017.0144},\n  month = {January},\n  pages = {15 (6 .)--15 (6 .)},\n  publisher = {IET Digital Library},\n  title = {{Extracting human settlement footprint from historical\ntopographic map series using context-based machine learning}},\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  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
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@misc{Chiang2016-hc,\n  author = {Chiang, Y Y},\n  howpublished = {2016 Conference on Complex Systems, Complex Systems Society},\n  location = {Amsterdam, Netherlands},\n  title = {{Exploiting Context in Cartographic Evolutionary Documents to\nExtract and Build Linked Spatial-Temporal Datasets}},\n  year = {2016}\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  author = {Chiang, Yao-Yi and Leyk, Stefan and Nazari, Narges Honarvar and\nMoghaddam, Sima and Tan, Tian Xiang},\n  doi = {10.1016/j.cageo.2016.04.013},\n  issn = {0098-3004},\n  journal = {Computers \\& geosciences},\n  month = {August},\n  pages = {21--35},\n  title = {{{Assessing the impact of graphical quality on automatic text\nrecognition in digital maps}}},\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  volume = {93},\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  author = {Duan, Weiwei and Chiang, Yao-Yi},\n  booktitle = {{{Proceedings of the 5th ACM SIGSPATIAL International Workshop on\nAnalytics for Big Geospatial Data}}},\n  doi = {10.1145/3006386.3006388},\n  isbn = {9781450345811},\n  month = {October},\n  pages = {7--13},\n  publisher = {ACM},\n  title = {{{Building knowledge graph from public data for predictive\nanalysis: a case study on predicting technology future in space\nand time}}},\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  year = {2016}\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  author = {Honarvar Nazari, Narges and Tan, Tian Xiang and Chiang, Yao-Yi},\n  booktitle = {{Proceedings of the Electronic Imaging, Document Recognition and\nRetrieval XXIII conference, Society for Imaging Science and\nTechnology}},\n  pages = {1--8},\n  series = {8},\n  title = {{Integrating Text Recognition for Overlapping Text Detection in\nMaps}},\n  url-file = {papers/Honarvar-Nazari-et-al.-2016-Integrating-Text-Recognition-for-Overlapping-Text-Detection-in-Maps.pdf},\n  year = {2016}\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  author = {Leyk, Stefan and Chiang, Yao-Yi},\n  booktitle = {{Proceedings of the 2016 AutoCarto}},\n  editor = {Freundschuh, Scott M},\n  pages = {100--110},\n  publisher = {AutoCarto},\n  title = {{{Information Extraction based on the Concept of Geographic\nContext}}},\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  year = {2016}\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
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@incollection{Park2016-ht,\n  author = {Park, W and Chiang, Y-Y and Lee, S J and Yu, K},\n  booktitle = {{Esri Map Book}},\n  publisher = {Esri},\n  title = {{Hot Spots of Tweets Related to Food, Entertainment, Work, and\nStudy in Gangnam Area of Seoul, Korea}},\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 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
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@article{Wu2016-io,\n  address = {New York, NY, USA},\n  author = {Wu, Wensheng and Meng, Weiyi and Su, Weifeng and Zhou, Guangyou\nand Chiang, Yao-Yi},\n  doi = {10.1145/2873061},\n  issn = {1559-1131},\n  journal = {ACM Trans. Web},\n  month = {April},\n  number = {2},\n  pages = {1--29},\n  publisher = {Association for Computing Machinery},\n  title = {{Q2P: Discovering Query Templates via Autocompletion}},\n  url = {https://doi.org/10.1145/2873061},\n  url-file = {papers/Wu-et-al.-2016-Q2P-Discovering-Query-Templates-via-Autocompletion.pdf},\n  volume = {10},\n  year = {2016}\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  author = {Yu, Ronald and Luo, Zexuan and Chiang, Yao-Yi},\n  booktitle = {{{Proceedings of the 23rd International Conference on Pattern\nRecognition}}},\n  title = {{{Recognizing Text On Historical Maps Using Maps From Multiple\nTime Periods}}},\n  url-file = {papers/Yu-et-al.-2016-Recognizing-Text-On-Historical-Maps-Using-Maps-From-Multiple-Time-Periods.pdf},\n  year = {2016}\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  author = {Zhang, Ying and Chiang, Yao-Yi and Knoblock, Craig and Li,\nChaopeng and Du, Liming and Liu, Shaowen and Singh, Sanjay},\n  booktitle = {{{Proceedings of the 19th AGILE International Conference on\nGeographic Information Science}}},\n  title = {{{An Automatic Approach for Building Place-Name Datasets from the\nWeb}}},\n  url-file = {papers/Zhang-et-al.-2016-An-Automatic-Approach-for-Building-Place-Name-Datasets-from-the-Web.pdf},\n  year = {2016}\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 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
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@inproceedings{Chiang2015-bm,\n  author = {Chiang, Yao-Yi and Leyk, Stefan},\n  booktitle = {{{Proceedings of the 27th International Cartographic Conference}}},\n  isbn = {9788588783119},\n  title = {{{Exploiting Online Gazetteer for Fully Automatic Extraction of\nCartographic Symbols}}},\n  year = {2015}\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  address = {New York, NY, USA},\n  author = {Chiang, Yao-Yi},\n  booktitle = {{{Proceedings of the 23rd SIGSPATIAL International Conference on\nAdvances in Geographic Information Systems}}},\n  doi = {10.1145/2820783.2820887},\n  isbn = {9781450339674},\n  location = {Bellevue, Washington},\n  month = {November},\n  pages = {16:1--16:4},\n  publisher = {ACM},\n  series = {GIS '15},\n  title = {{{Querying Historical Maps As a Unified, Structured, and Linked\nSpatiotemporal Source: Vision Paper}}},\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  year = {2015}\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
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@inproceedings{Chiang2015-xq,\n  author = {Chiang, Yao-Yi and Gehring, Sarah},\n  booktitle = {{{Proceedings of the 27th International Cartographic Conference}}},\n  isbn = {9788588783119},\n  title = {{{Semi-Automated Visualization of Spatial Context in Unstructured\nText}}},\n  year = {2015}\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  author = {Fernandes, Renuka and Chiang, Yao-Yi},\n  booktitle = {{{Proceedings of the 27th International Cartographic Conference}}},\n  isbn = {9788588783119},\n  title = {{{{Creating an Intuitive and Effective User Interface for Map\nProcessing in a Geographic Information System}}}},\n  year = {2015}\n}\n\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  author = {Ngo, Vivian and Swift, Jennifer and Chiang, Yao-Yi},\n  booktitle = {{{Proceedings of the 27th International Cartographic Conference}}},\n  isbn = {9788588783119},\n  title = {{{Visualizing Land Reclamation in Hong Kong: A Web Application}}},\n  url-file = {papers/Ngo-et-al.-2015-Visualizing-Land-Reclamation-in-Hong-Kong-A-Web-Application.pdf},\n  year = {2015}\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 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  author = {Chiang, Yao-Yi and Knoblock, Craig A},\n  doi = {10.1007/s10707-014-0203-9},\n  issn = {1384-6175, 1573-7624},\n  journal = {GeoInformatica},\n  language = {en},\n  month = {February},\n  number = {1},\n  pages = {1--27},\n  publisher = {Springer US},\n  title = {{{Recognizing Text in Raster Maps}}},\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  volume = {19},\n  year = {2014}\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  address = {New York, NY, USA},\n  author = {Chiang, Yao-Yi and Leyk, Stefan and Knoblock, Craig A},\n  doi = {10.1145/2557423},\n  issn = {0360-0300},\n  journal = {ACM Comput. Surv.},\n  month = {May},\n  number = {1},\n  pages = {1--44},\n  publisher = {Association for Computing Machinery},\n  title = {{A Survey of Digital Map Processing Techniques}},\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  volume = {47},\n  year = {2014}\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  author = {Chiang, Yao-Yi and Chioh, Phokgoan and Moghaddam, Sima},\n  booktitle = {{{Proceedings of the 8th International Conference on Geographic\nInformation Science}}},\n  pages = {264--269},\n  title = {{{{\\textbf{A Training-by-Example Approach for Symbol Spotting\nfrom Raster Maps }}}}},\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  year = {2014}\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  address = {New York, NY, USA},\n  author = {Chiang, Yao-Yi and Wu, Bo and Anand, Akshay and Akade, Ketan and\nKnoblock, Craig A},\n  booktitle = {{Proceedings of the 22nd ACM SIGSPATIAL International Conference\non Advances in Geographic Information Systems}},\n  doi = {10.1145/2666310.2666373},\n  isbn = {9781450331319},\n  location = {Dallas, Texas},\n  month = {November},\n  pages = {577--580},\n  publisher = {ACM},\n  series = {SIGSPATIAL '14},\n  title = {{A system for efficient cleaning and transformation of geospatial\ndata attributes}},\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  year = {2014}\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  address = {New York, New York, USA},\n  author = {Chiang, Yao-Yi and Moghaddam, Sima and Gupta, Sanjauli and\nFernandes, Renuka and Knoblock, Craig A},\n  booktitle = {{{Proceedings of the 22nd ACM SIGSPATIAL International Conference\non Advances in Geographic Information Systems}}},\n  doi = {10.1145/2666310.2666374},\n  isbn = {9781450331319},\n  month = {November},\n  pages = {581--584},\n  publisher = {ACM},\n  title = {{{From Map Images to Geographic Names}}},\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  year = {2014}\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  author = {Jaiswal, Ayush and Chiang, Yao-Yi and Knoblock, Craig A and Lan,\nLiang},\n  booktitle = {{{Proceedings of the 8th International Conference on Geographic\nInformation Science}}},\n  pages = {315--219},\n  title = {{{{Location Prediction With Sparse GPS Data}}}},\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  author = {Narayanan, S and Jaiswal, A and Chiang, Y Y and Geng, Y and\nKnoblock, C A and Szekely, P},\n  booktitle = {{Proceedings of the 2014 IEEE International Conference on Data\nMining Workshop}},\n  doi = {10.1109/ICDMW.2014.44},\n  isbn = {9781479942749},\n  issn = {2375-9232},\n  language = {English},\n  month = {December},\n  pages = {1076--1085},\n  publisher = {IEEE},\n  title = {{{{Integration and Automation of Data Preparation and Data\nMining}}}},\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  year = {2014}\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  address = {New York, NY, USA},\n  author = {Sathe, Mihir and Knoblock, Craig A and Chiang, Yao-Yi and\nHarris, Aaron},\n  booktitle = {{Proceedings of the 22nd ACM SIGSPATIAL International Conference\non Advances in Geographic Information Systems}},\n  doi = {10.1145/2666310.2666437},\n  isbn = {9781450331319},\n  location = {Dallas, Texas},\n  month = {November},\n  pages = {429--432},\n  publisher = {ACM},\n  series = {SIGSPATIAL '14},\n  title = {{A parallel query engine for interactive spatiotemporal analysis}},\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  year = {2014}\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
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@incollection{Chiang2013-fw,\n  address = {Berlin, Heidelberg},\n  author = {Chiang, Yao-Yi and Leyk, Stefan and Knoblock, Craig A},\n  booktitle = {{{Graphics Recognition. New Trends and Challenges: 9th\nInternational Workshop, GREC 2011, Seoul, Korea, September\n15-16, 2011, Revised Selected Papers}}},\n  doi = {10.1007/978-3-642-36824-0\\_3},\n  editor = {Kwon, Young-Bin and Ogier, Jean-Marc},\n  isbn = {9783642368233, 9783642368240},\n  issn = {0302-9743, 1611-3349},\n  location = {Seoul, Korea},\n  pages = {25--35},\n  publisher = {Springer Berlin Heidelberg},\n  series = {Lecture Notes in Computer Science, GREC'11},\n  title = {{Efficient and Robust Graphics Recognition from Historical Maps:\n9th International Workshop, GREC 2011, Seoul, Korea, September\n15-16, 2011, Revised Selected Papers}},\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  volume = {7423},\n  year = {2013}\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  author = {Chiang, Yao-Yi and Knoblock, Craig A},\n  doi = {10.1007/s10032-011-0177-1},\n  issn = {1433-2833, 1433-2825},\n  journal = {International Journal on Document Analysis and Recognition\n(IJDAR)},\n  language = {English},\n  month = {March},\n  number = {1},\n  pages = {55--81},\n  publisher = {Springer, Berlin / Heidelberg},\n  title = {{{A General Approach for Extracting Road Vector Data from Raster\nMaps}}},\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  volume = {16},\n  year = {2013}\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
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@inproceedings{Chiang2013-zo,\n  author = {Chiang, Yao-Yi},\n  booktitle = {{{Proceedings of the 26th International Cartographic Conference\n(ICC'13)}}},\n  isbn = {9781907075063},\n  pages = {838--838},\n  title = {{{{Strabo: A Complete System for Label Recognition in Maps}}}},\n  year = {2013}\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  address = {New York, NY, USA},\n  author = {Zhang, Ying and Chiang, Yao-Yi and Szekely, Pedro and Knoblock,\nCraig A},\n  booktitle = {{{Joint Proceedings of the Workshop on AI Problems and Approaches\nfor Intelligent Environments and Workshop on Semantic Cities}}},\n  doi = {10.1145/2516911.2516914},\n  isbn = {9781450323468},\n  location = {Beijing, China},\n  month = {August},\n  pages = {31--37},\n  publisher = {ACM},\n  series = {AIIP '13},\n  title = {{A semantic approach to retrieving, linking, and integrating\nheterogeneous geospatial data}},\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  year = {2013}\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  address = {Berlin, Heidelberg},\n  author = {Chiang, Yao-Yi and Knoblock, Craig A},\n  booktitle = {{Geographic Information Science}},\n  doi = {10.1007/978-3-642-33024-7\\_5},\n  editor = {Xiao, Ningchuan and Kwan, Mei-Po and Goodchild, Michael F and\nShekhar, Shashi},\n  isbn = {9783642330230, 9783642330247},\n  issn = {0302-9743, 1611-3349},\n  language = {English},\n  pages = {57--71},\n  publisher = {Springer Berlin Heidelberg},\n  series = {Lecture Notes in Computer Science},\n  title = {{Generating Named Road Vector Data from Raster Maps}},\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  volume = {7478},\n  year = {2012}\n}\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 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  author = {Chiang, Yao-Yi and Knoblock, Craig A},\n  booktitle = {{{Proceedings of the 20th International Conference on Pattern\nRecognition}}},\n  pages = {3199--3202},\n  title = {{{{An Approach for Recognizing Text Labels in Raster Maps}}}},\n  url-file = {papers/Chiang-and-Knoblock-2010-An-Approach-for-Recognizing-Text-Labels-in-Raster-Maps.pdf},\n  year = {2010}\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
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@inproceedings{Chiang2010-go,\n  address = {New York, New York, USA},\n  author = {Chiang, Yao-Yi and Knoblock, Craig A},\n  booktitle = {{{Proceedings of the 18th SIGSPATIAL International Conference on\nAdvances in Geographic Information Systems}}},\n  doi = {10.1145/1869790.1869889},\n  isbn = {9781450304283},\n  month = {November},\n  pages = {544--545},\n  publisher = {\\textbackslash ACM Request Permissions},\n  title = {{{Strabo: a System for Extracting Road Vector Data From Raster\nMaps}}},\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  year = {2010}\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
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@incollection{Chiang2010-rk,\n  author = {Chiang, Yao-Yi and Knoblock, Craig A},\n  booktitle = {{Graphics Recognition. Achievements, Challenges, and Evolution.\nGREC 2009. Lecture Notes in Computer Science}},\n  doi = {10.1007/978-3-642-13728-0\\_9},\n  editor = {Ogier, J M and ., Liu W and J., Llad{\\'o}s},\n  pages = {93--105},\n  publisher = {Springer Berlin Heidelberg},\n  title = {{Extracting Road Vector Data from Raster Maps}},\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  volume = {6020},\n  year = {2010}\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  author = {Knoblock, Craig A and Chen, Ching-Chien and Chiang, Yao-Yi and\nGoel, Aman and Michelson, Matthew and Shahabi, Cyrus},\n  booktitle = {{Document Recognition and Retrieval XVII}},\n  conference = {Document Recognition and Retrieval XVII},\n  doi = {10.1117/12.838967},\n  month = {January},\n  pages = {753402--753402--15},\n  publisher = {International Society for Optics and Photonics},\n  title = {{{A general approach to discovering, registering, and extracting\nfeatures from raster maps}}},\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  volume = {7534},\n  year = {2010}\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 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  address = {New York, NY, USA},\n  author = {Chiang, Yao-Yi and Knoblock, Craig A},\n  booktitle = {{{Proceedings of the 17th ACM SIGSPATIAL International Conference\non Advances in Geographic Information Systems}}},\n  doi = {10.1145/1653771.1653793},\n  isbn = {9781605586496},\n  location = {Seattle, Washington},\n  month = {November},\n  pages = {138--147},\n  publisher = {ACM},\n  series = {GIS '09},\n  title = {{{Classification of Raster Maps for Automatic Feature Extraction}}},\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  year = {2009}\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  author = {Chiang, Y Y and Knoblock, C A},\n  booktitle = {{Proceedings of the 2009 10th International Conference on\nDocument Analysis and Recognition}},\n  doi = {10.1109/ICDAR.2009.274},\n  issn = {1520-5363},\n  month = {July},\n  pages = {838--842},\n  title = {{{{A Method for Automatically Extracting Road Layers from Raster\nMaps}}}},\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  year = {2009}\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  author = {Chiang, Yao-Yi and Knoblock., Craig A and Shahabi, Cyrus and\nChen, Ching-Chien},\n  doi = {10.1007/s10707-008-0046-3},\n  issn = {1384-6175, 1573-7624},\n  journal = {GeoInformatica},\n  language = {en},\n  month = {June},\n  number = {2},\n  pages = {121--157},\n  publisher = {Springer US},\n  title = {{{{Automatic and Accurate Extraction of Road Intersections from\nRaster Maps}}}},\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  volume = {13},\n  year = {2009}\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  author = {Chiang, Yao-Yi and Knoblock, Craig A},\n  booktitle = {{{Proceedings of the 16th ACM SIGSPATIAL International Conference\non Advances in Geographic Information Systems}}},\n  doi = {10.1145/1463434.1463463},\n  isbn = {9781605583235},\n  month = {November},\n  pages = {1--10},\n  publisher = {ACM Press},\n  title = {{{{Automatic extraction of road intersection position,\nconnectivity, and orientations from raster maps}}}},\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  year = {2008}\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  author = {Chiang, Yao-Yi Yi and Knoblock, Craig A},\n  booktitle = {{Proceedings of the International Conference on Pattern\nRecognition}},\n  doi = {10.1109/ICPR.2006.368},\n  isbn = {9780769525211},\n  issn = {1051-4651},\n  month = {August},\n  pages = {1034--1037},\n  publisher = {IEEE},\n  title = {{{{Classification of Line and Character Pixels on Raster Maps\nUsing Discrete Cosine Transformation Coefficients and Support\nVector Machine}}}},\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  volume = {2},\n  year = {2006}\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  author = {Pal, U and Kimura, F and Roy, K and Pal, T},\n  booktitle = {{Proceedings of the 18th International Conference on Pattern\nRecognition (ICPR'06)}},\n  doi = {10.1109/ICPR.2006.971},\n  isbn = {9780769525211},\n  issn = {1051-4651},\n  month = {August},\n  pages = {873--876},\n  publisher = {IEEE Computer Society},\n  title = {{{{Recognition of English Multi-oriented Characters}}}},\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  volume = {2},\n  year = {2006}\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  author = {Shahabi, Cyrus and Chiang, Yao-Yi and Chung, Kelvin and Huang,\nKai-Chen and Khoshgozaran-Haghighi, Jeff and Craig A. Knoblocknd\nLee, Sung Chun and Neumann, Ulrich and Nevatia, Ram and Rihan,\nArjun and Thakkar, Snehal and You, Suya},\n  booktitle = {{{Proceedings of the IEEE International Conference on Multimedia\n\\& Expo}}},\n  pages = {93--96},\n  publisher = {IEEE},\n  title = {{{GEODEC: Eenableing Geospatial Decision Making}}},\n  url-file = {papers/Shahabi-et-al.-2006-GEODEC-Eenableing-Geospatial-Decision-Making.pdf},\n  year = {2006}\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 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  address = {New York, NY, USA},\n  author = {Chiang, Yao-Yi and Knoblock, Craig A and Chen, Ching-Chien},\n  booktitle = {{Proceedings of the 13th annual ACM international workshop on\nGeographic information systems}},\n  doi = {10.1145/1097064.1097102},\n  isbn = {9781595931467},\n  location = {Bremen, Germany},\n  month = {November},\n  pages = {267--276},\n  publisher = {ACM},\n  series = {GIS '05},\n  title = {{Automatic extraction of road intersections from raster maps}},\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  year = {2005}\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  author = {Desai, Sneha and Knoblock, Craig A and Chiang, Yao-Yi and Desai,\nKandarp and Chen, Ching-Chien},\n  booktitle = {{{Proceedings of the 2nd International Workshop on Geographic\nInformation Retrieval}}},\n  pages = {35--38},\n  title = {{{{Automatically identifying and georeferencing street maps on\nthe web}}}},\n  url-file = {papers/Desai-et-al.-2005-Automatically-identifying-and-georeferencing-street-maps-on-the-web.pdf},\n  year = {2005}\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  address = {New York, NY, USA},\n  author = {Chen, Ching-Chien and Knoblock, Craig A and Shahabi, Cyrus and\nChiang, Yao-Yi and Thakkar, Snehal},\n  booktitle = {{{Proceedings of the 12th Annual ACM International Workshop on\nGeographic Information Systems}}},\n  doi = {10.1145/1032222.1032231},\n  isbn = {9781581139792},\n  location = {Washington DC, USA},\n  month = {November},\n  pages = {47--56},\n  publisher = {ACM},\n  series = {GIS '04},\n  title = {{{{Automatically and Accurately Conflating Orthoimagery and\nStreet Maps}}}},\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  year = {2004}\n}\n\n
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