SHINTARO GOTOH
Department Department of Environment Systems, Faculty of Geo-Environmental Science Position Professor |
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Language | English |
Publication Date | 2022/09/30 |
Type | |
Peer Review | Peer reviewed |
Title | Disaster Area Detection by Deep Learning using UAV and Satellite Imagery |
Contribution Type | |
Journal | Proc. of Asian Conference on Remote sensing 2022 |
Journal Type | Another Country |
Publisher | Asian Conference on Remote sensing |
Responsible for | 論文統括、結果の評価 |
Authorship | Corresponding author |
Author and coauthor | Kazuaki AOKI, Shintaro GOTO. Chitomi SAKAI |
Details | Due to recent changes in the global environment, damage from disasters such as extreme
weather, typhoons, and heavy rainfall has been occurring frequently. In this study, we investigated a method for automatic detection of damaged areas from aerial photographs taken by UAV and satellite photographs in order to obtain detailed information on the damage at an early stage. We applied a deep learning-based method to detect damaged areas from orthomosaic images of disaster-stricken areas using data taken by a drone in the area affected by Typhoon 19 and other disasters. The construction of a system that automatically detects damaged areas is expected to lead to an early understanding situations and cost reductions. |