Following the occurrence of a typhoon, quick damage assessment can facilitate the quick Spirulina 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 Compression Socks 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.