改进U-Net模型在滑坡区域识别中的应用OA

Loess hilly regions are characterized by complex topography and frequent landslide disasters.We proposed an automated landslide identification method based on improved U-Net model.Compared to the original model,the improved U-Net model achieved an approximately 4.0%increase in overall accuracy and an approximately 8.9%increase in landslide classification accuracy.Focusing on Xiji County,Ningxia Hui Autonomous Region,China,we used the improved U-Net model to identify the landslide areas in high-resolution optical remote sensing images.A total of 7 553 landslide patches were extracted,classified,and the spatial distribution patterns and causes of landslide areas were analyzed.We evaluated the recognition results by combining multi-source datasets with manual visual interpretation.The results demonstrate that the improved U-Net model achieves notable improvements in both identification accuracy and generalization capability,particularly under complex terrain conditions.This method provides a new technical approach for rapid landslide identification in loess hilly regions,offering practical significance for geological disaster prevention and mitigation.