Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning

Abstract

Following the occurrence of a typhoon, quick damage assessment can facilitate the quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following Typhoon Faxai in 2019, to automatically detect and evaluate the roof damage. This study comprised three parts: training a deep learning model, detecting the roof damage using a trained model, and classifying the level of roof damage. The detection object comprised a roof outline, blue tarps, and a completely destroyed roof. The roofs were divided into three categories: without damage, with blue tarps, and completely destroyed. The F value obtained using the proposed method was higher than those obtained using other methods. In addition, it can be further divided into five levels from levels 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide a certain reference for real-time detection of roof damage after the occurrence of a typhoon.