Flood vulnerability assessment of urban buildings based on integrating high-resolution remote sensing and street view images

Urban flood risk management requires an extensive investigation of the vulnerability characteristics of buildings. Large-scale field surveys usually cost a lot of time and money, while satellite remote sensing and street view images can provide information on the tops and facades of buildings respectively. Thereupon, this paper develops a building vulnerability assessment framework using remote sensing and street view features. Specifically, a UNet-based semantic segmentation model, FSA-UNet (Fusion-Self-Attention-UNet) is proposed to integrate remote sensing and street view features and the vulnerability information contained in the images is fully exploited. And the building vulnerability index is generated to provide the spatial distribution characteristics of urban building vulnerability. The experiment shows that the mIoU of the proposed model can reach 82% for building vulnerability classification in Hefei, China, which is more accurate than the traditional semantic seg-mentation models. The results indicate that the integration of street view and remote sensing image features can improve the ability of building vulnerability assessment, and the model proposed in this study can better capture the correlation features of multi-angle images through the self-attention mechanism and combines hierarchy features and edge information to improve the classification effect. This study can support for disaster manage-ment and urban planning.

Automated summary

This paper proposes a framework for assessing building vulnerability using remote sensing and street view features. By integrating these features, the vulnerability information contained in the images is fully utilized to generate a building vulnerability index, providing spatial distribution characteristics of urban building vulnerability. The results show that the proposed model improves the accuracy of building vulnerability classification and captures correlation features of multi-angle images.