{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T13:33:32Z","timestamp":1772717612443,"version":"3.50.1"},"reference-count":93,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Technology of Mexico (TecNM)","award":["ITESLRIO\/PGP-01"],"award-info":[{"award-number":["ITESLRIO\/PGP-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth\u2019s surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases.<\/jats:p>","DOI":"10.3390\/rs15123009","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T02:03:18Z","timestamp":1686276198000},"page":"3009","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Sentinel-1 SAR Images and Deep Learning for Water Body Mapping"],"prefix":"10.3390","volume":"15","author":[{"given":"Fernando","family":"Pech-May","sequence":"first","affiliation":[{"name":"Department of Computer Science, TecNM: Instituto Tecnol\u00f3gico Superior de los R\u00edos, Balanc\u00e1n 86930, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8242-0695","authenticated-orcid":false,"given":"Ra\u00fal","family":"Aquino-Santos","sequence":"additional","affiliation":[{"name":"Universidad Tecnol\u00f3gica de Manzanillo, Las Humedades s\/n Col. Salagua, Manzanillo 28869, Mexico"}]},{"given":"Jorge","family":"Delgadillo-Partida","sequence":"additional","affiliation":[{"name":"Universidad Tecnol\u00f3gica de Manzanillo, Las Humedades s\/n Col. Salagua, Manzanillo 28869, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"ref_1","unstructured":"Centre for Research on the Epidemiology of Disasters (CRED) (2021). 2021 Disasters in Numbers, CRED. Technical Report."},{"key":"ref_2","unstructured":"Guha-Sapir, D., Below, R., and Hoyois, P. (2023, March 04). EM-DAT: The CRED\/OFDA International Disaster Database. Available online: https:\/\/www.emdat.be\/."},{"key":"ref_3","unstructured":"Wallemacq, P., and House, R. (2018). Economic Losses, Poverty and Disasters (1998\u20132017), Centre for Research on the Epidemiology of Disasters United Nations Office for Disaster Risk Reduction. Technical Report."},{"key":"ref_4","unstructured":"Paz, J., Jim\u00e9nez, F., and S\u00e1nchez, B. (2018). 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