Study on physical correction based on topographic information for inundation area mapping using AI

Abstract

In recent years, Japan has seen an increase in the risk of water damage due to more frequent heavy rainfall, resulting in a more severe scale of damage. Satellite images are expected to be useful for quickly understanding the full extent of damage during large-scale water damage, and research has been conducted on detecting flooded areas from satellite images using deep learning. However, it is necessary to improve detection accuracy and ensure the physical validity of AI inference results. In this paper, we aim to address these challenges by examining a correction method based on topographical information for flood inundation area segmentation results obtained from deep learning models. Experimental results showed that adding a process to increase flood inundation detection based on topographical information improves recall without affecting metrics such as IoU, thereby reducing the likelihood of missing flood inundation areas.