Physics-Informed Deep Learning with GLCM-Integrated Loss for Building Damage Assessment Using Remote Sensing

Accurate damage assessment following a natural disaster is crucial for guiding effective response and recovery efforts. In this study, we present a deep learning framework that employs multi-temporal fusion of pre- and post-disaster remote sensing imagery, using a shared-weight ResNet50 architecture as the model backbone. To improve both predictive performance and physical realism, we incorporated a physics-informed loss function that integrates texture energy derived from a GrayLevel Co-Occurrence Matrix (GLCM). This additional loss term penalizes outputs that fail to exhibit expected rubble-like texture patterns, thereby encouraging more physically consistent predictions. Model performance was assessed using several evaluation metrics, including confusion matrices, average accuracy, F1 scores, xView2 scores, and multi-class ROC curves. Experimental results show that the inclusion of the GLCM-based physicsinformed loss resulted in measurable improvements in both damage detection and classification accuracy-achieving a 7% gain in average accuracy and a 8.57% increase in xView2 score compared to baseline models without the physics constraint.