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Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

1
DaSSIP Team—LISITE, ISEP, 28 rue Notre Dame des Champs, 75006 Paris, France
2
Université Paris 13, Sorbonne Paris Cité, LIPN—CNRS UMR 7030, 99 av. J.-B. Clément, 93430 Villetaneuse, France
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2019, 11(9), 1123; https://doi.org/10.3390/rs11091123
Received: 9 April 2019 / Revised: 7 May 2019 / Accepted: 8 May 2019 / Published: 10 May 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

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. View Full-Text
Keywords: change detection; deep learning; Tsunami; damage mapping; remote sensing change detection; deep learning; Tsunami; damage mapping; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sublime, J.; Kalinicheva, E. Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami. Remote Sens. 2019, 11, 1123.

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