Development of a building damage estimation model and disaster investigation support system using earthquake damage survey images

Abstract

We developed a model that estimates building parts and damage types using semantic segmentation based on disaster survey images taken immediately after the 1995 southern Hyogo Prefecture earthquake, the 2011 off the Pacific Coast of Tohoku earthquake, the 2016 Kumamoto earthquake, the 2019 Hokkaido Eastern Iburi earthquake, and the 2024 Noto Peninsula earthquake. The building part estimation model achieved recalls and precisions of over 60-70%, confirming a relatively high estimation accuracy. For the damage type estimation model, the covered area achieved recalls of approximately 80% and precisions of approximately 70%, while the collapsed areas achieved recalls of approximately 50% and precisions of approximately 80%, demonstrating relatively high accuracy. However, the accuracy for damaged parts and soil damage was somewhat lower. Additionally, we prototyped a disaster survey support system that runs on a portable computer and confirmed that real-time display of building components and damage types is feasible.