TITLE:
An Enhanced U-Net Model for Large-Scale Landslide Prediction Using Multi-Source Remote Sensing Data and Physical Risk Assessment
AUTHORS:
Liping Zheng, Liangjun Zhao, Chunlong Fu, Kaiwen Xiao, Moran Chou
KEYWORDS:
Landslide Prediction, Multi-Source Remote Sensing, Landslide Risk Index (LSI), Enhanced U-Net, Disaster Mitigation
JOURNAL NAME:
Journal of Computer and Communications,
Vol.13 No.8,
August
8,
2025
ABSTRACT: Rainfall-induced landslides threaten mountainous regions globally, yet existing models face challenges in real-time, large-scale prediction due to dependency on post-event data. This study presents an enhanced U-Net model integrating multi-source remote sensing data and physical Risk assessment for proactive landslide forecasting. We construct a novel six-channel dataset (soil moisture, slope, elevation, and anthropogenic indices) and propose a dynamic Landslide Risk Index (LSI) model that dynamically weights rainfall intensity and duration. The enhanced U-Net architecture uniquely combines hybrid convolution (standard + dilated) to capture multi-scale features and a Multi-Channel Spatial Attention (MCSA) mechanism to prioritize critical spatial-channel relationships. Experimental results demonstrate state-of-the-art performance: the LSI model achieves an AUC of 0.968, while the enhanced U-Net attains a Dice coefficient of 66.7%, surpassing DeepLabV3 + by 0.8%, with real-time processing at 21.66 FPS. Validations in Sichuan, China, reveal the model’s ability to identify both historical and previously unrecognized high-Risk zones, exemplified by predictions in Junlian County where steep, vegetated slopes were flagged despite no prior landslides. This work bridges the gap between retrospective analysis and proactive Risk management, offering a scalable solution for disaster mitigation.