Xu, J.; Zeng, F.; Liu, W.; Takahashi, T. Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning. Appl. Sci.2022, 12, 4912.
Xu, J.; Zeng, F.; Liu, W.; Takahashi, T. Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning. Appl. Sci. 2022, 12, 4912.
Xu, J.; Zeng, F.; Liu, W.; Takahashi, T. Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning. Appl. Sci.2022, 12, 4912.
Xu, J.; Zeng, F.; Liu, W.; Takahashi, T. Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning. Appl. Sci. 2022, 12, 4912.
Abstract
Following the occurrence of a typhoon, quick damage assessment related to residents can facilitate 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 the Typhoon Faxai in 2019 to automatically detect and evaluate the roof damage. This study comprised three parts: training deep learning model, detecting the roof damage using trained model, and classifying the level of roof damage. The detection object comprised roof outline, blue tarps, and roof completely destroyed. The roofs were divided into three categories: roof without damage, roof with blue tarps and roof 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 5 levels from level 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide certain reference for the real-time detection of the roof damage after the occurrence of a typhoon.
Keywords
Deep Learning; Aerial photo; Typhoon Faxai; roof damage; detection; classification
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
