{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:56:48Z","timestamp":1777733808258,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2105133, 2126474"],"award-info":[{"award-number":["2105133, 2126474"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008893","name":"University of Iowa","doi-asserted-by":"publisher","award":["ISSSF"],"award-info":[{"award-number":["ISSSF"]}],"id":[{"id":"10.13039\/100008893","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000203","name":"U.S. Geological Survey","doi-asserted-by":"publisher","award":["G21AC10207"],"award-info":[{"award-number":["G21AC10207"]}],"id":[{"id":"10.13039\/100000203","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006785","name":"Google","doi-asserted-by":"publisher","award":["AI for Social Good"],"award-info":[{"award-number":["AI for Social Good"]}],"id":[{"id":"10.13039\/100006785","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SAFER-SIM"},{"DOI":"10.13039\/100007921","name":"University of Pittsburgh","doi-asserted-by":"publisher","award":["Pitt Momentum Seeding Grant"],"award-info":[{"award-number":["Pitt Momentum Seeding Grant"]}],"id":[{"id":"10.13039\/100007921","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008510","name":"University of Maryland","doi-asserted-by":"publisher","award":["DRI"],"award-info":[{"award-number":["DRI"]}],"id":[{"id":"10.13039\/100008510","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,2]]},"DOI":"10.1145\/3474717.3483970","type":"proceedings-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T22:59:44Z","timestamp":1636066784000},"page":"313-323","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Spatial-Net"],"prefix":"10.1145","author":[{"given":"Yiqun","family":"Xie","sequence":"first","affiliation":[{"name":"University of Maryland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Jia","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Bao","sequence":"additional","affiliation":[{"name":"University of Iowa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Iowa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Yu","sequence":"additional","affiliation":[{"name":"Washington State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahul","family":"Ghosh","sequence":"additional","affiliation":[{"name":"University of Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Praveen","family":"Ravirathinam","sequence":"additional","affiliation":[{"name":"University of Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161602"},{"key":"e_1_3_2_1_2_1","volume-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","author":"Badrinarayanan Vijay","year":"2017","unstructured":"Vijay Badrinarayanan , Alex Kendall , and Roberto Cipolla . 2017 . Segnet: A deep convolutional encoder-decoder architecture for image segmentation . IEEE transactions on pattern analysis and machine intelligence 39, 12 (2017), 2481--2495. Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39, 12 (2017), 2481--2495."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/0022-4146.00146"},{"key":"e_1_3_2_1_4_1","unstructured":"CDL 2021. USDA Cropland Data Layer. https:\/\/www.nass.usda.gov\/Research_and_Science\/Cropland\/SARS1a.php.  CDL 2021. USDA Cropland Data Layer. https:\/\/www.nass.usda.gov\/Research_and_Science\/Cropland\/SARS1a.php."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Liang-Chieh Chen Yukun Zhu George Papandreou Florian Schroff and Hartwig Adam. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV. 801--818.  Liang-Chieh Chen Yukun Zhu George Papandreou Florian Schroff and Hartwig Adam. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV. 801--818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Shuhan Chen Xiuli Tan Ben Wang and Xuelong Hu. 2018. Reverse attention for salient object detection. In ECCV.  Shuhan Chen Xiuli Tan Ben Wang and Xuelong Hu. 2018. Reverse attention for salient object detection. In ECCV.","DOI":"10.1007\/978-3-030-01240-3_15"},{"key":"e_1_3_2_1_7_1","unstructured":"Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML.  Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML."},{"key":"e_1_3_2_1_8_1","unstructured":"GEOGLAM 2021. GEOGLAM Global Crop Monitoring. https:\/\/earthobservations.org\/geoglam.php.  GEOGLAM 2021. GEOGLAM Global Crop Monitoring. https:\/\/earthobservations.org\/geoglam.php."},{"key":"e_1_3_2_1_9_1","volume-title":"Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach. ACM Trans. on Intelligent Systems and Technology (TIST)","author":"Gupta Jayant","year":"2021","unstructured":"Jayant Gupta , Carl Molnar , Yiqun Xie , Joe Knight , and Shashi Shekhar . 2021. Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach. ACM Trans. on Intelligent Systems and Technology (TIST) ( 2021 ). (in press). Jayant Gupta, Carl Molnar, Yiqun Xie, Joe Knight, and Shashi Shekhar. 2021. Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach. ACM Trans. on Intelligent Systems and Technology (TIST) (2021). (in press)."},{"key":"e_1_3_2_1_10_1","volume-title":"ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems.","author":"Gupta Jayant","year":"2020","unstructured":"Jayant Gupta , Yiqun Xie , and Shashi Shekhar . 2020 . Towards Spatial Variability Aware Deep Neural Networks (SVANN): A Summary of Results . In ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems. Jayant Gupta, Yiqun Xie, and Shashi Shekhar. 2020. Towards Spatial Variability Aware Deep Neural Networks (SVANN): A Summary of Results. In ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems."},{"key":"e_1_3_2_1_11_1","unstructured":"Xiaowei Jia Guruprasad Nayak Ankush Khandelwal Anuj Karpatne and Vipin Kumar. 2019. Classifying heterogeneous sequential data by cyclic domain adaptation: An application in land cover detection. In SDM. SIAM.  Xiaowei Jia Guruprasad Nayak Ankush Khandelwal Anuj Karpatne and Vipin Kumar. 2019. Classifying heterogeneous sequential data by cyclic domain adaptation: An application in land cover detection. In SDM. SIAM."},{"key":"e_1_3_2_1_12_1","article-title":"Spatial ensemble learning for heterogeneous geographic data with class ambiguity","volume":"10","author":"Jiang Zhe","year":"2019","unstructured":"Zhe Jiang , Arpan Man Sainju , Yan Li , Shashi Shekhar , and Joseph Knight . 2019 . Spatial ensemble learning for heterogeneous geographic data with class ambiguity . ACM Trans. on Intelligent Systems and Technology (TIST) 10 , 4 (2019). Zhe Jiang, Arpan Man Sainju, Yan Li, Shashi Shekhar, and Joseph Knight. 2019. Spatial ensemble learning for heterogeneous geographic data with class ambiguity. ACM Trans. on Intelligent Systems and Technology (TIST) 10, 4 (2019).","journal-title":"ACM Trans. on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 2014 SIAM Intl. Conf. on Data Mining. SIAM, 253--261","author":"Karpatne Anuj","year":"2014","unstructured":"Anuj Karpatne , Ankush Khandelwal , Shyam Boriah , and Vipin Kumar . 2014 . Predictive learning in the presence of heterogeneity and limited training data . In Proceedings of the 2014 SIAM Intl. Conf. on Data Mining. SIAM, 253--261 . Anuj Karpatne, Ankush Khandelwal, Shyam Boriah, and Vipin Kumar. 2014. Predictive learning in the presence of heterogeneity and limited training data. In Proceedings of the 2014 SIAM Intl. Conf. on Data Mining. SIAM, 253--261."},{"key":"e_1_3_2_1_14_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 ( 2012 ), 1097--1105. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012), 1097--1105."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Jonathan Long Evan Shelhamer and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In ICCV.  Jonathan Long Evan Shelhamer and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In ICCV.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_1_16_1","volume-title":"How does multiple testing correction work? Nature biotechnology 27, 12","author":"Noble William S","year":"2009","unstructured":"William S Noble . 2009. How does multiple testing correction work? Nature biotechnology 27, 12 ( 2009 ), 1135--1137. William S Noble. 2009. How does multiple testing correction work? Nature biotechnology 27, 12 (2009), 1135--1137."},{"key":"e_1_3_2_1_17_1","unstructured":"Hyeonwoo Noh et al. 2015. Learning deconvolution network for semantic segmentation. In ICCV.  Hyeonwoo Noh et al. 2015. Learning deconvolution network for semantic segmentation. In ICCV."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Joseph Redmon Santosh Divvala Ross Girshick and Ali Farhadi. 2016. You only look once: Unified real-time object detection. In CVPR.  Joseph Redmon Santosh Divvala Ross Girshick and Ali Farhadi. 2016. You only look once: Unified real-time object detection. In CVPR.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00108"},{"key":"e_1_3_2_1_21_1","unstructured":"Sentinel-2 Multispectral Imagery 2021. GEE. https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/COPERNICUS_S2_SR#bands.  Sentinel-2 Multispectral Imagery 2021. GEE. https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/COPERNICUS_S2_SR#bands."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2756547"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2009.2016214"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397536.3422217"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274895.3274901"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313577"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.223"}],"event":{"name":"SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems","location":"Beijing China","acronym":"SIGSPATIAL '21","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"]},"container-title":["Proceedings of the 29th International Conference on Advances in Geographic Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474717.3483970","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474717.3483970","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474717.3483970","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:46Z","timestamp":1750191106000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474717.3483970"}},"subtitle":["A Self-Adaptive and Model-Agnostic Deep Learning Framework for Spatially Heterogeneous Datasets"],"short-title":[],"issued":{"date-parts":[[2021,11,2]]},"references-count":27,"alternative-id":["10.1145\/3474717.3483970","10.1145\/3474717"],"URL":"https:\/\/doi.org\/10.1145\/3474717.3483970","relation":{},"subject":[],"published":{"date-parts":[[2021,11,2]]},"assertion":[{"value":"2021-11-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}