Detection of Earthquake-Induced Building Damages Using Remote Sensing Data and Deep Learning: A Case Study of Mashiki Town, Japan
Muhammad Salem, Ahmed Gomaa, Naoki Tsurusaki
Abstract
Natural disasters cause extensive economic losses every year. Rapid detection of earthquake-induced building damages is crucial for disaster response. Remote sensing (RS) has been widely used to assess the impacts of natural disasters i.e. earthquakes and its implications on building damages. Deep Learning (DL) techniques have become increasingly popular for detecting building damages from RS data and have achieved significant success in detecting disaster implications. This paper examines the ability of DL to detect building damages caused by Kumamoto earthquake in Mashiki town, Japan using RS data. The findings indicate that the newly trained model demonstrated effective performance in discriminating between different levels of building damages, including no damage, damage, and collapse. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>