{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:52:21Z","timestamp":1778860341255,"version":"3.51.4"},"reference-count":160,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"name":"National Science Foundation","award":["OCI-0725070 and ACI-1238993"],"award-info":[{"award-number":["OCI-0725070 and ACI-1238993"]}]},{"name":"NSF","award":["IIS 21-31335, OAC 21-30835, and DBI 20-21898"],"award-info":[{"award-number":["IIS 21-31335, OAC 21-30835, and DBI 20-21898"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>\n            Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satellite imagery often includes additional spectral bands beyond red, green, and blue, and must be joined to other geospatial data sources that may have differing coordinate systems, bounds, and resolutions. To help realize the potential of deep learning for remote sensing applications, we introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides data loaders for a variety of benchmark datasets, composable datasets for uncurated geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery. TorchGeo is also the first library to provide pretrained models for multispectral satellite imagery (e.g., models that use all bands from the Sentinel-2 satellites), allowing for advances in transfer learning on downstream remote sensing tasks with limited labeled data. We use TorchGeo to create reproducible benchmark results on existing datasets and benchmark our proposed method for preprocessing geospatial imagery on the fly. TorchGeo is open source and available on GitHub:\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/microsoft\/torchgeo\">https:\/\/github.com\/microsoft\/torchgeo<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3707459","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T06:30:13Z","timestamp":1734330613000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["TorchGeo: Deep Learning With Geospatial Data"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0468-5006","authenticated-orcid":false,"given":"Adam J.","family":"Stewart","sequence":"first","affiliation":[{"name":"Department of Aerospace and Geodesy, Technical University of Munich","place":["Munich, Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1975-4454","authenticated-orcid":false,"given":"Caleb","family":"Robinson","sequence":"additional","affiliation":[{"name":"AI for Good Research Lab, Microsoft","place":["Redmond, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9273-7303","authenticated-orcid":false,"given":"Isaac A.","family":"Corley","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, The University of Texas at San Antonio","place":["San Antonio, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5722-5273","authenticated-orcid":false,"given":"Anthony","family":"Ortiz","sequence":"additional","affiliation":[{"name":"AI for Good Research Lab, Microsoft","place":["Redmond, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9654-3178","authenticated-orcid":false,"given":"Juan M.","family":"Lavista Ferres","sequence":"additional","affiliation":[{"name":"AI for Good Research Lab, Microsoft","place":["Redmond, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8211-6989","authenticated-orcid":false,"given":"Arindam","family":"Banerjee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois Urbana-Champaign","place":["Urbana, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,25]]},"reference":[{"issue":"7","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.3390\/rs12071156","article-title":"ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD)","volume":"12","author":"Abrams Michael","year":"2020","unstructured":"Michael Abrams, Robert Crippen, and Hiroyuki Fujisada. 2020. 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