Comprehensive information provision system for evacuation support during disasters

Abstract

This study proposes a web-based comprehensive information provision system to support evacuation dur- ing disasters, driven by the increasing frequency of heavy rainfall and associated flood risks in Japan. The system integrates flood detection from aerial imagery using the deep learning model YOLOv9 and visualize flooded roads on OpenStreetMap (OSM). Furthermore, it provides real-time location acquisition via GPS, shelter information, and elevation differences along evacuation routes from the user’s current location to designated shelters. Verifications using flood data from Sakura City and field tests in Takamatsu City demonstrated the system’s capability to suggest safe routes avoiding flooded roads, facilitate current loca- tion awareness, and aid in shelter selection considering elevation differences. This enables evacuees to make quicker and safer decisions based on multi-faceted information.