Identification of Landslides in the Hilly Areas of Eastern China Using High-Resolution GF-2 Images and Deep Learning Models

Small, dispersed, and vegetated creeping landslides in hilly areas of eastern China hinder traditional remote sensing and detection. To address this, this study takes Yixian County (Anhui Province) as a representative area. Based on high-resolution GF-2 satellite images, three deep learning models embedded with the Squeeze-and-Excitation (SE) attention mechanism (ResNet18-SE, VGG13-SE, UNet-SE) were developed and compared with the original UNet model. Combined with field investigation, landslide mapping and accuracy assessment were conducted to evaluate the feature extraction capabilities of four models. The results indicate that the UNet-SE model achieved optimal performance (Precision: 0.911, Recall: 0.685, F1-score: 0.782, Kappa: 0.730, IoU: 0.643). Its F1-score exceeds ResNet18-SE, VGG13-SE, and the original UNet by 8%, 3%, and 5%, respectively, proving superior regional adaptability and generalization performance. Additional verification on creeping landslides in Kecun Town (Yixian County) and post-earthquake landslides in Lushan County (Sichuan Province) further confirms the reliability of the UNet-SE model. Furthermore, Frequency Ratio (FR), Random Forest (RF), and SHapley Additive exPlanations (SHAP) were adopted to reveal the correlation between landslide occurrence and seven geological-environmental factors. Landslides are most susceptible to develop at elevations of 400–500 m, NDVI values of 0.4–0.5, slopes below 10°, east and northeast aspects, 300–500 m away from rivers, 500–1000 m away from faults, and areas covered by soft sedimentary lithology. Distance from faults, distance from rivers, and elevation are identified as the three favorable conditional factors. In conclusion, the proposed landslide detection framework can provide reliable spatial data and technical references for regional geological hazard prevention, ecological conservation and sustainable development in hilly areas.