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.

This study examines the habits and attitudes of construction sector employees in Türkiye regarding the use of personal protective equipment (PPE). A comprehensive face-to-face survey was conducted with 398 randomly selected employees from small- and medium-sized construction companies across various regions of Türkiye. The survey aimed to identify the frequency of PPE usage and the factors influencing its use and to provide actionable suggestions for improving PPE compliance. The results indicate that while a majority of workers recognize the importance of PPE, actual usage rates are inconsistent. Key factors affecting PPE use include the ergonomics and comfort of the equipment as well as worker perceptions and attitudes. Notably, the study found that the discomfort associated with PPE and the belief that it slows down work pace are significant barriers to its regular use. Additionally, the research highlights the need for a robust punishment–reward system to enforce PPE compliance and suggests that more stringent legislative measures are required to enhance occupational health and safety (OHS) standards in the construction industry. This study contributes to the existing body of knowledge by providing a detailed analysis of PPE usage in the Turkish construction sector and offering practical recommendations for policymakers and industry stakeholders. Implementing these suggestions can lead to improved worker safety and reduced occupational accidents.