{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:56:37Z","timestamp":1782316597582,"version":"3.54.5"},"reference-count":60,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques\u2014which have successfully been applied to other segmentation tasks\u2014have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94.<\/jats:p>","DOI":"10.3390\/rs12152422","type":"journal-article","created":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T10:16:49Z","timestamp":1595931409000},"page":"2422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":144,"title":["A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9422-0236","authenticated-orcid":false,"given":"Lisa","family":"Knopp","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, D-82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1155-723X","authenticated-orcid":false,"given":"Marc","family":"Wieland","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, D-82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michaela","family":"R\u00e4ttich","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, D-82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6400-361X","authenticated-orcid":false,"given":"Sandro","family":"Martinis","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, D-82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1126\/science.aam7672","article-title":"Using fire to promote biodiversity","volume":"355","author":"Kelly","year":"2017","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2019.02.013","article-title":"Historical background and current developments for mapping burned area from satellite Earth observation","volume":"225","author":"Chuvieco","year":"2019","journal-title":"Remote Sens. 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