Abstract
Abstract When natural disasters occur, damage to roads and bridges not only disrupts transportation networks and endangers public safety but also affects the efficiency of rescue and evacuation work. Therefore, in the disaster response stage, the ability to quickly assess the situation in disaster-affected areas is extremely important. With the rapid development of technology, remote sensing provides a more efficient method for creating disaster maps compared to traditional methods. However, when dealing with large-scale data, there are still limitations in traditional image analysis methods based on remote sensing data. To solve this problem, this study proposes a binary deep learning framework to determine whether the bridges or roads in remote sensing data have been damaged, and provides an automated method for image analysis based on remote sensing data. According to the results, the EfficientNet-b1-based deep learning model excelled with an accuracy of 94.09%, which is considered sufficient for precisely conducting damage assessment after disasters. For future improvements, we suggest replacing binary classification with more precise semantic segmentation or object detection models, which can better identify and locate damaged areas, thereby enhancing the capability of damage detection.