Pixel-level Identification and Collapse of Post-earthquake Building Complexes Based on Satellite Remote Sensing Images

Due to the obvious diversity and complexity of damage patterns, geometries, and spatial scales of urban building complex earthquake hazards, conventional identification and assessment methods are less generalizable in real post-earthquake scenarios. Compared with time-range signals such as kinetic acceleration, image/video data provide a new source of perceptual information for accurately assessing the post-earthquake damage of urban building complexes. In order to realize the integrated, comprehensive, and rapid identification and assessment of the structural damage of urban buildings after an earthquake, this paper proposes a geometrically constrained deep learning framework for seismic damage identification and assessment of buildings based on computer vision, and systematically carries out a multi-scale seismic damage identification and assessment method that associates the “building group, building unit, and structural component" with the "building group, building unit, and structural component”. This paper proposes a geometrically constrained deep learning framework for seismic damage identification and assessment based on computer vision, and systematically carries out the research on the identification and assessment of “building groups-building units-structural components”. A method for fine identification of densely distributed small-target buildings and rapid assessment of collapse state after earthquake based on satellite remote sensing images at high altitude is proposed. A semantic segmentation network for post-earthquake building cluster identification and assessment is built, the influence law of the weight coefficients of the GCE loss on the segmentation performance of the model is systematically investigated, and the geometric feature optimization performance of the GCE loss in the training process and the multi-level feature extraction ability are analyzed, which verifies the effectiveness and accuracy of the geometrically constrained deep learning method for multi-scale seismic damage identification of buildings proposed in this paper.