Effective identification of precursors to large landslide instability is critical for hazard assessment and early warning. However, conventional displacement-based approaches are often limited by the spatiotemporal resolution of monitoring data and the inherent spatial heterogeneity of landslide deformation. Here, we propose a precursor identification framework based on the spatiotemporal evolution of landslide surface scars. This approach characterises progressive failure through temporal variations in geometric and topological features of surface morphology, capturing the transition from stable to unstable states. Multi-temporal optical imagery is used in conjunction with a vision foundation model, guided by domain knowledge, to enable automated delineation and continuous tracking of landslide scars. The extracted geometric and topological descriptors provide a quantitative basis for representing landslide evolution and detecting precursory signals. Application to representative cases demonstrates that a critical precursor signal is identified on 25 July 2018, approximately 2.5 months prior to failure of the Baige landslide. For the Sela landslide, which did not undergo system-scale failure, a transition on 7 June 2019 corresponds to externally induced localised acceleration. These results indicate that precursor signals can be identified from the perspective of surface morphological evolution, offering a complementary observational basis for landslide early warning and risk assessment.
Zhou et al. (2026) studied this question.