Deep Learning-Based Technique for Building Damage Extraction and Mapping from Ground-Level Images Using Visible Remote Sensing Indices and Edge Angle Dispersion as Input Features

We developed a deep learning model for automated extraction and assessment of earthquake damage from dashcam and post-disaster images.By combining a custom-designed deep multi-layer perceptron model with an enhanced feature extraction methodology, we accurately classify image patches into "No Damage" (Class 0) and "Damage" (Class 1).The proposed model incorporates a rich set of image-based features, including color statistics, edge properties, and texture descriptors, along with strategies to mitigate class imbalance.Experimental results demonstrate the model's high performance in identifying damaged areas, particularly its excellent recall for the "Damage" class, which is critical for rapid disaster response and damage mapping.