A Multilevel Damage Assessment Framework for Mixed-Hazard Buildings With Global Spatial Feature Guidance Module and Change Feature Attention in VHR Remote Sensing Images

After a natural disaster, it is crucial to locate the affected areas and assess building damage for emergency rescue operations. Automatic analysis technology based on remote sensing imagery improves the effectiveness of building damage assessment. However, the existing methods face challenges in determining building damage levels across different types of disasters owing to complex appearance features in remote sensing images. These methods for extracting building damage features are insufficient and lack attention to subtle differences between some visually similar categories. Therefore, this study proposes a multilevel damage assessment (MLDA) universal framework for mixed-hazard buildings. MLDA designs a global spatial feature guidance (GSFG) module to extract high-level semantic features. GSFG pays more attention to the spatial deformation of buildings by long-distance context modeling. It extracts common features of multiple damaged buildings by calculating the deformation similarity under a global perspective, accurately determining their damage levels without considering the impact of the type of disaster. Then, MLDA constructs a damage change feature attention (DCFA) module to enhance heterogeneous features between pre- and postdisaster images. DCFA identifies subtle differences between different damage levels in the process of multiscale feature fusion by channel and spatial dual attention mechanisms. Compared with the state-of-the-art methods, MLDA assesses the damage level of mixed disaster-stricken buildings effectively. Especially for minor damaged buildings, the accuracy rate improved by about 2.3%. The source code is available at
https://github.com/DongzheHan-cv/MLDA
.