{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T17:11:51Z","timestamp":1772557911550,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T00:00:00Z","timestamp":1557446400000},"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>Post-disaster damage mapping is an essential task following tragic events such as hurricanes, earthquakes, and tsunamis. It is also a time-consuming and risky task that still often requires the sending of experts on the ground to meticulously map and assess the damages. Presently, the increasing number of remote-sensing satellites taking pictures of Earth on a regular basis with programs such as Sentinel, ASTER, or Landsat makes it easy to acquire almost in real time images from areas struck by a disaster before and after it hits. While the manual study of such images is also a tedious task, progress in artificial intelligence and in particular deep-learning techniques makes it possible to analyze such images to quickly detect areas that have been flooded or destroyed. From there, it is possible to evaluate both the extent and the severity of the damages. In this paper, we present a state-of-the-art deep-learning approach for change detection applied to satellite images taken before and after the Tohoku tsunami of 2011. We compare our approach with other machine-learning methods and show that our approach is superior to existing techniques due to its unsupervised nature, good performance, and relative speed of analysis.<\/jats:p>","DOI":"10.3390\/rs11091123","type":"journal-article","created":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T03:57:07Z","timestamp":1557719827000},"page":"1123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":125,"title":["Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0508-8550","authenticated-orcid":false,"given":"J\u00e9r\u00e9mie","family":"Sublime","sequence":"first","affiliation":[{"name":"DaSSIP Team\u2014LISITE, ISEP, 28 rue Notre Dame des Champs, 75006 Paris, France"},{"name":"Universit\u00e9 Paris 13, Sorbonne Paris Cit\u00e9, LIPN\u2014CNRS UMR 7030, 99 av. J.-B. Cl\u00e9ment, 93430 Villetaneuse, France"}]},{"given":"Ekaterina","family":"Kalinicheva","sequence":"additional","affiliation":[{"name":"DaSSIP Team\u2014LISITE, ISEP, 28 rue Notre Dame des Champs, 75006 Paris, France"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mori, N., Takahashi, T., Yasuda, T., and Yanagisawa, H. (2011). Survey of 2011 Tohoku earthquake tsunami inundation and run-up. Geophys. Res. Lett., 38.","DOI":"10.1029\/2011GL049210"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sublime, J., Troya-Galvis, A., and Puissant, A. (2017). Multi-Scale Analysis of Very High Resolution Satellite Images Using Unsupervised Techniques. Remote Sens., 9.","DOI":"10.3390\/rs9050495"},{"key":"ref_3","first-page":"311","article-title":"Geometric Distortion and Correction Methods for Finding Key Points:A Survey","volume":"4","author":"Patel","year":"2016","journal-title":"Int. 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