Research on Early Interpretation Techniques for Potential Landslide Terrain Based on Deep Generative Adversarial Reinforcement Learning

Landslides are among the most common and destructive geological hazards, posing severe threats to human life, property, and environmental stability. Early identification of potential landslide areas is essential for effective disaster prevention and early warning. Traditional interpretation methods, which rely on field surveys and manual terrain analysis, are time-consuming, labor-intensive, and highly subjective. Although recent deep learning-based approaches have improved automation, their performance remains limited in complex terrain settings where landslide textures are blurred, positive samples are scarce, and spatial structures are intricate. To address these challenges, this study proposes a Deep Generative Adversarial Reinforcement Learning framework for Landslide Segmentation (DGARL-LS). The proposed model integrates the regional exploration capability of reinforcement learning with the fine-grained image reconstruction power of generative adversarial networks. A bidirectional feedback mechanism enables dynamic co-optimization between the generator and discriminators, enhancing model robustness under weak supervision and sparse reward conditions. Real topographic and elevation data from Xipu and Fengjie and others Typical landslide area, Chongqing, were used to construct a Curvature–Slope (CS) stereoscopic image dataset incorporating slope, curvature, and contour features. Data augmentation strategies including rotation, flipping, and sharpening expanded the dataset to 3,864 samples. During training, multi-strategy optimization was adopted, including hyperparameter tuning, dual-discriminator loss collaboration, and cosine annealing learning rate scheduling, leading to more stable convergence and improved segmentation accuracy. Experimental results demonstrate that the CS stereoscopic images effectively enhances terrain feature representation. The proposed DGARL-LS method outperforms conventional models in terms of accuracy and recall, particularly in regions with blurred textures and indistinct boundaries. The results verify the feasibility and effectiveness of the proposed approach for intelligent landslide recognition. This study provides a novel technical pathway for potential landslide interpretation and offers valuable insights into integrating topographic representation with deep learning architectures.