Landslide Susceptibility Assessment Based on a Deep Learning-Derived Landslide Inventory in Moxi Town, Sichuan, China

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Abstract
Landslides are characterized by strong suddenness and a wide range of damage; accurate prediction of their susceptibility is an important prerequisite for regional risk prevention and control. To address the difficulties in acquiring landslide inventories in complex terrain areas and the insufficient interpretability of existing prediction models, this study proposes a landslide susceptibility assessment (LSA) framework that integrates automated sample detection and interpretability analysis. The proposed framework is applied to Moxi Town, a typical alpine valley area in Sichuan Province, China. A Mask R-CNN instance segmentation model was introduced to achieve automated detection of landslide samples, resulting in a high-quality inventory containing 923 landslides. Based on the spatial relationships between the landslide inventory and influencing factors, a convolutional neural network (CNN) landslide susceptibility assessment model incorporating Shapley Additive exPlanations (SHAP) interpretability analysis was constructed. The CNN model was further compared with random forest (RF) and extreme gradient boosting (XGBoost) machine learning models. The results show that the AUC value of the CNN model has increased by 4.3% and 3.2% compared with the RF and XGBoost models, respectively, and it significantly reduces the pretzel effect of landslide susceptibility mapping (LSM). The results validate the reliability of the proposed framework, which can provide technical support for landslide disaster prevention and monitoring.
Funding Information
  • National Natural Science Foundation of China (U22A20565)
  • National Natural Science Foundation of China (42401101)
  • Sichuan Science and Technology Program (2026NSFSC1135)
  • Science and Technology Open Foundation of Sichuan Society of Surveying and Mapping Geoinformation (CCX202501)
  • central government guided local science and technology development fund projects (202502ZYDF034)
  • Supply–Demand Matching for Employment and Talent Cultivation Project of the Ministry of Education of China (2025050790134)
  • Technology Innovation Center for Emergency Surveying and Mapping (MNR (YJCX-2026-YB-03-01))

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