Detecting building damage caused by typhoon from optical satellite imagery using deep learning

Abstract

Natural disasters such as typhoons and heavy rains have become more frequent and severe in recent years. If the damage of each house in a large-scale disaster can be automatically assessed, it will lead to a reduction in the workload of professional workers such as on-site inspectors and repair person. In this study, we propose a deep learning method using optical satellite images before and after a disaster, focusing on the estimation of damage to individual houses caused by a typhoon. The region used for training is Chiba Prefecture during Typhoon No. 15 (FAXAI) in 2019, and the damage to buildings includes not only roofs, which are relatively easy to detect damage from satellite images, but also other parts of buildings such as exterior walls. From the viewpoint of classifying the degree of damage, the simplest binary classification was considered as the classification task. The multimodal method, which combines images and building attribute information, has a higher recall, i.e., it is harder to miss the damage, than the method that uses only images.