{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:38:27Z","timestamp":1770755907665,"version":"3.50.0"},"reference-count":53,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T00:00:00Z","timestamp":1708992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Minist\u00e8re des Ressources Naturelles et de la For\u00eat du Qu\u00e9bec","award":["610023-09"],"award-info":[{"award-number":["610023-09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite observations provide critical data for a myriad of applications, but automated information extraction from such vast datasets remains challenging. While artificial intelligence (AI), particularly deep learning methods, offers promising solutions for land cover classification, it often requires massive amounts of accurate, error-free annotations. This paper introduces a novel approach to generate a segmentation task dataset with minimal human intervention, thus significantly reducing annotation time and potential human errors. \u2018Samples\u2019 extracted from actual imagery were utilized to construct synthetic composite images, representing 10 segmentation classes. A DeepResUNet was solely trained on this synthesized dataset, eliminating the need for further fine-tuning. Preliminary findings demonstrate impressive generalization abilities on real data across various regions of Quebec. We endeavored to conduct a quantitative assessment without reliance on manually annotated data, and the results appear to be comparable, if not superior, to models trained on genuine datasets.<\/jats:p>","DOI":"10.3390\/rs16050818","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T05:20:32Z","timestamp":1709011232000},"page":"818","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Synthetic Data for Sentinel-2 Semantic Segmentation"],"prefix":"10.3390","volume":"16","author":[{"given":"\u00c9tienne","family":"Clabaut","sequence":"first","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9557-6907","authenticated-orcid":false,"given":"Samuel","family":"Foucher","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1487-2945","authenticated-orcid":false,"given":"Yacine","family":"Bouroubi","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1867-7530","authenticated-orcid":false,"given":"Micka\u00ebl","family":"Germain","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111366","DOI":"10.1016\/j.rse.2019.111366","article-title":"Half a century of satellite remote sensing of sea-surface temperature","volume":"233","author":"Minnett","year":"2019","journal-title":"Remote Sens. 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