Timely and accurate structural damage assessment is essential for effective post‐earthquake response, especially in large‐scale disasters such as the February 2023 Türkiye earthquake. Manual inspections are slow and subjective, while current deep learning (DL) approaches remain limited by binary classification, weak contextual modeling, and high computational demands. The proposed model is evaluated on a high‐resolution UAV‐based earthquake damage dataset collected from post‐disaster urban regions in Türkiye. STCHMDA‐CVT achieves 99.27% precision, recall, and F1‐score, outperforming six traditional machine learning models, six deep CNNs, and five state‐of‐the‐art attention‐based architectures. These results position STCHMDA‐CVT as a robust, efficient, and interpretable solution for automated structural damage assessment in post‐earthquake scenarios. Gradient‐weighted class activation mapping (Grad‐CAM) visualizations further enhance interpretability by highlighting structural regions critical to the model's decision‐making. While the framework is validated on Türkiye data, it can be adapted to other seismic contexts through region‐specific calibration, such as fine‐tuning with local building typologies, construction materials, and seismic intensity distributions.
Mowla et al. (2026) studied this question.