{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:10:11Z","timestamp":1750194611015,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:00:00Z","timestamp":1604361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,11,3]]},"DOI":"10.1145\/3397536.3422232","type":"proceedings-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T22:17:39Z","timestamp":1606256259000},"page":"593-596","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Geospatial Data Gateways to Support the Operational Application of Deep Learning Models"],"prefix":"10.1145","author":[{"given":"Aiman","family":"Soliman","sequence":"first","affiliation":[{"name":"NCSA, University of Illinois, Urbana, Illinois"}]},{"given":"Jeffrey","family":"Terstriep","sequence":"additional","affiliation":[{"name":"NCSA, University of Illinois, Urbana, Illinois"}]}],"member":"320","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"GLOBAL SCIENCE AND TECHNOLOGY INC GREENBELT MD","author":"Evans John D","year":"2005","unstructured":"John D Evans and Myra J Bambacus . Nasa's earth-sun system gateway: an open standards-based portal to geospatial data and services. Technical report , GLOBAL SCIENCE AND TECHNOLOGY INC GREENBELT MD , 2005 . John D Evans and Myra J Bambacus. Nasa's earth-sun system gateway: an open standards-based portal to geospatial data and services. Technical report, GLOBAL SCIENCE AND TECHNOLOGY INC GREENBELT MD, 2005."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2010.04.001"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.2481\/dsj.2.164"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2006.82"},{"key":"e_1_3_2_1_5_1","volume-title":"Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152: 166--177","author":"Ma Lei","year":"2019","unstructured":"Lei Ma , Yu Liu , Xueliang Zhang , Yuanxin Ye , Gaofei Yin , and Brian Alan Johnson . Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152: 166--177 , 2019 . Lei Ma, Yu Liu, Xueliang Zhang, Yuanxin Ye, Gaofei Yin, and Brian Alan Johnson. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152: 166--177, 2019."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.11.042609"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2017.2762307"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3086952"},{"key":"e_1_3_2_1_9_1","volume-title":"Blocks and fuel: Frameworks for deep learning. arXiv preprint arXiv:1506.00619","author":"Merri\u00ebnboer Bart Van","year":"2015","unstructured":"Bart Van Merri\u00ebnboer , Dzmitry Bahdanau , Vincent Dumoulin , Dmitriy Serdyuk , David Warde-Farley , Jan Chorowski , and Yoshua Bengio . Blocks and fuel: Frameworks for deep learning. arXiv preprint arXiv:1506.00619 , 2015 . Bart Van Merri\u00ebnboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, and Yoshua Bengio. Blocks and fuel: Frameworks for deep learning. arXiv preprint arXiv:1506.00619, 2015."},{"issue":"1","key":"e_1_3_2_1_10_1","first-page":"1","article-title":"open source processing of remote sensing images. Open Geospatial Data","volume":"2","author":"Grizonnet Manuel","year":"2017","unstructured":"Manuel Grizonnet , Julien Michel , Victor Poughon , Jordi Inglada , Micka\u00ebl Savin-aud, and R\u00e9mi Cresson . Orfeo toolbox : open source processing of remote sensing images. Open Geospatial Data , Software and Standards , 2 ( 1 ): 1 -- 8 , 2017 . Manuel Grizonnet, Julien Michel, Victor Poughon, Jordi Inglada, Micka\u00ebl Savin-aud, and R\u00e9mi Cresson. Orfeo toolbox: open source processing of remote sensing images. Open Geospatial Data, Software and Standards, 2(1): 1--8, 2017.","journal-title":"Software and Standards"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2867949"},{"key":"e_1_3_2_1_12_1","volume-title":"Keras Blog","author":"Chollet Francois","year":"2016","unstructured":"Francois Chollet . Building powerful image classification models using very little data . Keras Blog , 2016 . Francois Chollet. Building powerful image classification models using very little data. Keras Blog, 2016."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3356471.3365240"},{"key":"e_1_3_2_1_14_1","volume-title":"The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621","author":"Perez Luis","year":"2017","unstructured":"Luis Perez and Jason Wang . The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 , 2017 . Luis Perez and Jason Wang. The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621, 2017."},{"key":"e_1_3_2_1_15_1","volume-title":"Improved relation classification by deep recurrent neural networks with data augmentation. arXiv preprint arXiv:1601.03651","author":"Xu Yan","year":"2016","unstructured":"Yan Xu , Ran Jia , Lili Mou , Ge Li , Yunchuan Chen , Yangyang Lu , and Zhi Jin . Improved relation classification by deep recurrent neural networks with data augmentation. arXiv preprint arXiv:1601.03651 , 2016 . Yan Xu, Ran Jia, Lili Mou, Ge Li, Yunchuan Chen, Yangyang Lu, and Zhi Jin. Improved relation classification by deep recurrent neural networks with data augmentation. arXiv preprint arXiv:1601.03651, 2016."},{"key":"e_1_3_2_1_16_1","volume-title":"Sxl: Spatially explicit learning of geographic processes with auxiliary tasks. arXiv preprint arXiv:2006.10461","author":"Klemmer Konstantin","year":"2020","unstructured":"Konstantin Klemmer and Daniel B Neill . Sxl: Spatially explicit learning of geographic processes with auxiliary tasks. arXiv preprint arXiv:2006.10461 , 2020 . Konstantin Klemmer and Daniel B Neill. Sxl: Spatially explicit learning of geographic processes with auxiliary tasks. arXiv preprint arXiv:2006.10461, 2020."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0912-1"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377000.3377002"},{"key":"e_1_3_2_1_19_1","volume-title":"Geoai: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond","author":"Janowicz Krzysztof","year":"2020","unstructured":"Krzysztof Janowicz , Song Gao , Grant McKenzie , Yingjie Hu , and Budhendra Bhaduri . Geoai: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond , 2020 . Krzysztof Janowicz, Song Gao, Grant McKenzie, Yingjie Hu, and Budhendra Bhaduri. Geoai: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond, 2020."}],"event":{"name":"SIGSPATIAL '20: 28th International Conference on Advances in Geographic Information Systems","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"],"location":"Seattle WA USA","acronym":"SIGSPATIAL '20"},"container-title":["Proceedings of the 28th International Conference on Advances in Geographic Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3397536.3422232","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3397536.3422232","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:34Z","timestamp":1750193254000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3397536.3422232"}},"subtitle":["Vision Paper"],"short-title":[],"issued":{"date-parts":[[2020,11,3]]},"references-count":19,"alternative-id":["10.1145\/3397536.3422232","10.1145\/3397536"],"URL":"https:\/\/doi.org\/10.1145\/3397536.3422232","relation":{},"subject":[],"published":{"date-parts":[[2020,11,3]]},"assertion":[{"value":"2020-11-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}