Tracking the Spatiotemporal Evolution of Landslide Scars Using a Vision Foundation Model: A Novel and Universal Framework
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917005045530624 |
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| author | Zhou, Meijun Mei, Gang Ma, Zhengjing Xu, Nengxiong Peng, Jianbing |
| author_facet | Zhou, Meijun Mei, Gang Ma, Zhengjing Xu, Nengxiong Peng, Jianbing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10084 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tracking the Spatiotemporal Evolution of Landslide Scars Using a Vision Foundation Model: A Novel and Universal Framework Zhou, Meijun Mei, Gang Ma, Zhengjing Xu, Nengxiong Peng, Jianbing Computer Vision and Pattern Recognition 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. |
| title | Tracking the Spatiotemporal Evolution of Landslide Scars Using a Vision Foundation Model: A Novel and Universal Framework |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.10084 |