학술논문
Detection of Earthquake-Induced Building Damages Using Remote Sensing Data and Deep Learning: A Case Study of Mashiki Town, Japan
Document Type
Conference
Author
Source
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2023 - 2023 IEEE International. :2350-2353 Jul, 2023
Subject
Language
ISSN
2153-7003
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. 1