用视觉大模型追踪滑坡演变，实现早期预警与长期评估

Tracking the spatiotemporal evolution of large-scale landslide scars is critical for understanding the evolution mechanisms and failure precursors, enabling effective early-warning. However, most existing studies have focused on single-phase or pre- and post-failure dual-phase landslide identification. Although these approaches delineate post-failure landslide boundaries, it is challenging to track the spatiotemporal evolution of landslide scars. To address this problem, this study proposes a novel and universal framework for tracking the spatiotemporal evolution of large-scale landslide scars using a vision foundation model. The key idea behind the proposed framework is to reconstruct discrete optical remote sensing images into a continuous video sequence. This transformation enables a vision foundation model, which is developed for video segmentation, to be used for tracking the evolution of landslide scars. The proposed framework operates within a knowledge-guided, auto-propagation, and interactive refinement paradigm to ensure the continuous and accurate identification of landslide scars. The proposed framework was validated through application to two representative cases: the post-failure Baige landslide and the active Sela landslide (2017-2025). Results indicate that the proposed framework enables continuous tracking of landslide scars, capturing both failure precursors critical for early warning and post-failure evolution essential for assessing secondary hazards and long-term stability.