Geometry-guided Semantic Segmentation for Post-Earthquake Buildings Using Optical Remote Sensing Images

Deep-learning-based automatic recognition of post-earthquake damage for urban buildings is increasingly in demand for rapid and precise assessment of seismic hazards from optical remote sensing images. In this study, a novel loss function fusing geometric consistency constraint (GCC) with cross-entropy (CE) loss is designed for post-earthquake building segmentation with complex geometric features across multiple scales. Specifically, the GCC loss incorporates three critical components, namely, split line length, curvature, and area, and enables the exact extraction of the geometric constraints of boundary and region for damaged buildings. Through the optimization of multiple key coefficients of GCC loss, the proposed method achieves significant performance improvements in semantic segmentation, which is attributed to the enhanced ability to identify and capture the pixel relationship near the boundary. Merging GCC in the loss function enables faster and more accurate convergence of predicted values towards the ground truth during the training process, surpassing the performance of the CE loss alone. The results show that the combination of GCC and CE losses achieves the largest validation mIoU of 86.98% for damaged buildings segmentation, which facilitates post-earthquake assessment with high accuracy. Moreover, incorporating GCC leads to more precise and robust seismic damage segmentation by effectively improving edge closure, removing internal noise, and reducing false-positive and false-negative misrecognition. In addition, the GCC term further validates the effectiveness of improving segmentation tasks for other networks (e.g., DeepLabv3+). The GCC-derived method exhibits its desirable performance on segmentation accuracy, portability, and universality for building recognition with complex geometric features and post-earthquake scenes.