This study focuses on the identification of collapsed buildings in satellite images after earthquakes through deep learning-based image segmentation models. The performance of four different architectures, namely U-Net, LinkNet, FPN, and PSPNet, was evaluated using various performance metrics, such as accuracy, precision, recall, F1 score, specificity, AUC, and IoU. The study used satellite images taken from the area located in the south and southeast of Türkiye covering the eleven provinces which are most affected by the Mw 7.7 Pazarcık (Kahramanmaraş) and Mw 7.6 Elbistan (Kahramanmaraş) earthquakes. The results indicated that FPN and U-Net were the best-performing models depending on the performance metric of interest. FPN achieved the highest accuracy and specificity scores, as well as the best precision score, while U-Net achieved the best recall and F1 score values, as well as the best AUC and IoU scores. The training and validation accuracy and loss curves were analyzed, and the results indicated that all four models achieved an accuracy value of over 96%. The FPN model outperformed the others in terms of accurately segmenting images while maintaining a low loss value. This study provides insights into the potential of deep learning-based image segmentation models in disaster management and can be useful for future research in this field.

The efficient and precise identification of cracks in masonry stone structures caused by natural or human-induced factors within a specific region holds significant importance in detecting damage and subsequent secondary harm. In recent times, remote sensing technologies have been actively employed to promptly identify crack regions during repair and reinforcement activities. Enhanced image resolution has enabled more accurate and sensitive detection of these areas. This research presents a novel approach utilizing deep learning techniques for crack area detection in cellphone images, achieved through segmentation and object detection methods. The developed model, named the CAM-K-SEG segmentation model, combines Grad-CAM visualization and K-Mean clustering approaches with pre-trained convolutional neural network models. A comprehensive dataset comprising photographs of numerous historical buildings was utilized for training the model. To establish a comparative analysis, the widely used U-Net segmentation model was employed. The training and testing datasets for the developed technique were meticulously annotated and masked. The evaluation of the results was based on the Intersection-over-Union (IoU) metric values. Consequently, it was concluded that the CAM-K-SEG model exhibits suitability for object recognition and localization, whereas the U-Net model is well-suited for crack area segmentation.