Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review. Yang, L., MacEachren, A., Mitra, P., & Onorati, T. ISPRS International Journal of Geo-Information, 7(2):65, Multidisciplinary Digital Publishing Institute, 2, 2018.
Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review [pdf]Paper  Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review [link]Website  abstract   bibtex   
This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives—for application-based opportunities, with emphasis on those that address big data with geospatial components.
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 title = {Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review},
 type = {article},
 year = {2018},
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 keywords = {active learning,centered computing,class classification,deep learning,geographic information retrieval,human,image classification,label classification,machine learning,multi,text classification,visual analytics},
 pages = {65},
 volume = {7},
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 month = {2},
 publisher = {Multidisciplinary Digital Publishing Institute},
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 abstract = {This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives—for application-based opportunities, with emphasis on those that address big data with geospatial components.},
 bibtype = {article},
 author = {Yang, Liping and MacEachren, Alan and Mitra, Prasenjit and Onorati, Teresa},
 journal = {ISPRS International Journal of Geo-Information},
 number = {2}
}
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