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Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image (v1.0), modern high-resolution satellite image data sets (Maxar WorldView data), and instructions\/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100\u2013200 times its normal velocity. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in six simplified classes.<\/jats:p>","DOI":"10.3390\/rs16111854","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T03:49:03Z","timestamp":1716436143000},"page":"1854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Combining \u201cDeep Learning\u201d and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5694-4698","authenticated-orcid":false,"given":"Ute C.","family":"Herzfeld","sequence":"first","affiliation":[{"name":"Geomathematics, Remote Sensing, and Cryospheric Sciences Laboratory, Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO 80309, USA"},{"name":"Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA"},{"name":"Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO 80309, USA"}]},{"given":"Lawrence J.","family":"Hessburg","sequence":"additional","affiliation":[{"name":"Geomathematics, Remote Sensing, and Cryospheric Sciences Laboratory, Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO 80309, USA"},{"name":"Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2271-6854","authenticated-orcid":false,"given":"Thomas M.","family":"Trantow","sequence":"additional","affiliation":[{"name":"Geomathematics, Remote Sensing, and Cryospheric Sciences Laboratory, Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO 80309, USA"}]},{"given":"Adam N.","family":"Hayes","sequence":"additional","affiliation":[{"name":"Geomathematics, Remote Sensing, and Cryospheric Sciences Laboratory, Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO 80309, USA"},{"name":"Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"ref_1","unstructured":"Wagner, W. 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