Landslide Identification from Post-Earthquake High-Resolution Remote Sensing Images Based on ResUNet–BFA

The integration of deep learning and remote sensing for the rapid detection of landslides from high-resolution remote sensing imagery plays a crucial role in post-disaster emergency response. However, the availability of publicly accessible deep learning datasets specifically for landslide detection remains limited, posing challenges for researchers in meeting task requirements. To address this issue, this study develops and releases a deep learning landslide dataset using Google Earth imagery, focusing on the impact zones of the 2008 Wenchuan Ms8.0 earthquake, the 2014 Ludian Ms6.5 earthquake, and the 2017 Jiuzhaigou Ms7.0 earthquake as the research areas. The dataset contains 2727 samples with a spatial resolution of 1.06 m. To enhance landslide recognition, a lightweight boundary-focused attention (BFA) mechanism designed using the Canny operator is adopted. This mechanism improves the model’s ability to emphasize landslide edge features and is integrated with the ResUNet model, forming the ResUNet–BFA architecture for landslide identification. The experimental results indicate that the ResUNet–BFA model outperforms widely used algorithms in extracting landslide boundaries and details, resulting in fewer misclassifications and omissions. Additionally, compared with conventional attention mechanisms, the BFA achieves superior performance, producing recognition results that more closely align with actual labels.