Earthquakes can cause considerable damage to transportation infrastructure, affecting emergency response. Timely detection of road debris is important in such situations. The purpose of this research is to address the challenges related to the rapid detection of collapsed areas and road blockages. Deep learning has become an essential tool in remote sensing and image processing, offering improved capabilities for classification, and segmentation from high-resolution imagery. Its integration into post-disaster analysis enables more accurate assessments compared to traditional methods. This study examines the performance of three commonly used semantic segmentation models Unet, Attention Unet (AttUnet), and ResUnet++ for road debris detection. Two earthquake events were used as case studies: the 2023 earthquake in Osmaniye, Türkiye (Mw 7.8), captured by the Pleiades satellite, and the 2017 earthquake in Sarpol-e Zahab, Kermanshah, Iran (Mw 7.3), captured by a Phantom 4 Pro drone. The models were evaluated using three metrics: Intersection over Union (IoU), Recall, and Accuracy. The results show that ResUnet++ achieved higher performance compared to Unet and AttUnet in both cases. For the Osmaniye dataset, ResUnet++ reached an IoU of 80.81%, Recall of 78.88%, and Accuracy of 96.12%. On the Sarpol-e Zahab dataset, it obtained an IoU of 81.62%, Recall of 80.28%, and Accuracy of 97.24%. Unet and AttUnet performed at lower levels across all evaluated metrics. This comparison provides a clear assessment of model performance in post-earthquake debris detection tasks and contributes to ongoing work in the application of deep learning and high-resolution imagery for geospatial analysis in disaster response contexts.