{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:36:43Z","timestamp":1775666203453,"version":"3.50.1"},"reference-count":99,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"publisher","award":["2019-04669"],"award-info":[{"award-number":["2019-04669"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"publisher","award":["2020-01722"],"award-info":[{"award-number":["2020-01722"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001862","name":"Formas","doi-asserted-by":"publisher","award":["2019-04669"],"award-info":[{"award-number":["2019-04669"]}],"id":[{"id":"10.13039\/501100001862","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001862","name":"Formas","doi-asserted-by":"publisher","award":["2020-01722"],"award-info":[{"award-number":["2020-01722"]}],"id":[{"id":"10.13039\/501100001862","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Southern Africa experiences a great number of wildfires, but the dependence on low-resolution products to detect and quantify fires means both that there is a time lag and that many small fire events are never identified. This is particularly relevant in miombo woodlands, where fires are frequent and predominantly small. We developed a cutting-edge deep-learning-based approach that uses freely available Sentinel-2 data for near-real-time, high-resolution fire detection in Mozambique. The importance of Sentinel-2 main bands and their derivatives was evaluated using TreeNet, and the top five variables were selected to create three training datasets. We designed a UNet architecture, including contraction and expansion paths and a bridge between them with several layers and functions. We then added attention gate units (AUNet) and residual blocks and attention gate units (RAUNet) to the UNet architecture. We trained the three models with the three datasets. The efficiency of all three models was high (intersection over union (IoU) &gt; 0.85) and increased with more variables. This is the first time an RAUNet architecture has been used to detect fire events, and it performed better than the UNet and AUNet models\u2014especially for detecting small fires. The RAUNet model with five variables had IoU = 0.9238 and overall accuracy = 0.985. We suggest that others test the RAUNet model with large datasets from different regions and other satellites so that it may be applied more broadly to improve the detection of wildfires.<\/jats:p>","DOI":"10.3390\/rs15051342","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T03:02:32Z","timestamp":1678071752000},"page":"1342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["High-Resolution Semantic Segmentation of Woodland Fires Using Residual Attention UNet and Time Series of Sentinel-2"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2882-850X","authenticated-orcid":false,"given":"Zeinab","family":"Shirvani","sequence":"first","affiliation":[{"name":"Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgr\u00e4nd 17, 901 83 Ume\u00e5, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8048-8792","authenticated-orcid":false,"given":"Omid","family":"Abdi","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, 00014 Helsinki, Finland"}]},{"given":"Rosa C.","family":"Goodman","sequence":"additional","affiliation":[{"name":"Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgr\u00e4nd 17, 901 83 Ume\u00e5, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1111\/j.1466-8238.2010.00568.x","article-title":"Climate and the inter-annual variability of fire in southern Africa: A meta-analysis using long-term field data and satellite-derived burnt area data","volume":"19","author":"Archibald","year":"2010","journal-title":"Glob. 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