Multi‐view street view image fusion for city‐scale assessment of wind damage to building clusters
Donglian Gu, Qianwen Shuai, Naidan Zhang, Nan Jin, Z.X. Zheng, Zhen Xu, Yingjie Xu
Abstract
Global warming amplifies the risk of wind-induced building damage in coastal cities worldwide. Existing numerical methods for predicting building damage under winds have been limited to virtual environments, given the prohibitive costs associated with establishing city-scale window inventories. Hence, this study introduces a cost-effective workflow for wind damage prediction of real built environments, where the window inventory can be established with the multi-view street view image (SVI) fusion and artificial intelligence large model. The feasibility of the method is demonstrated based on two real-world urban areas. Notably, the proposed multi-view method surpasses both the single-view and aerial image-based methods in terms of window recognition accuracy. The increasing availability of SVIs opens up opportunities for applying the proposed method not only in disaster prevention but also in environmental and energy topics, thereby enhancing the resilience of cities and communities from multiple perspectives.