YOLO-Based Landslide Identification and Causal Inference Using Double Machine Learning in Longyan, Fujian

Landslides and their associated secondary hazards present substantial threats to both public infrastructure and resident safety. The rapid and accurate identification of large-scale landslide events remains a critical challenge in the field of engineering geology. In this study, a YOLO-based deep learning model is developed for landslide identification relying on a training dataset constructed using the satellite imagery of Longyan City, Fujian Province, in 2024. Adopting the double machine learning model, we examine the causal inference relationships between landslide and causative factors, including rainfall (R), mean Normalized Difference Vegetation Index (NDVI) and Distance to roads (DRoa). A total of 1185 landslides is identified in 2024, covering an area of approximately 31.02 km2. The landslides are predominantly concentrated in Shanghang, Wuping, Changting, and the southern part of Xinluo. The landslides mainly correspond to elevations around 300–500 m, slopes among the interval of [10°, 25°], and annual rainfall intensities ranging from 1600 m to 1700 mm. The top five key factors for landslide occurrence in descending order are NDVI, R, DRoa, Distance to Rivers (DRiv) and Aspect (A), in terms SHAP values. Causal inference analysis reveals that the rainfall in June and July shows significant positive causal effects to landslide, which is consistent with the physical mechanism of rainfall-induced landslide and the landslide data reported by the government. The framework proposed and the findings in this study offer valuable technical and theoretical support for landslide identification and risk assessment in southwestern Fujian.