Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. By applying this framework to ascending and descending Sentinel-1 data in the complex terrain of the Jishi Mountain region, we first introduce geometric distortion masking and a C-Index deformation consistency check, which enables the reliable identification of 530 active landslides, with 154 detected in both orbits. Second, we employ a local parallel flow model to invert the landslide movement geometry without relying on DEM-derived prior assumptions, successfully retrieving the two-dimensional (sliding and normal direction) deformation fields for all 154 consistent landslides. Finally, by synthesizing these 2D deformation patterns with geomorphological features, we achieve a systematic classification of movement types, categorizing them into retrogressive translational (31), progressive translational (66), rotational (19), composite (24), and earthflows (14). This integrated methodology provides a validated, transferable solution for deciphering landslide mechanisms and assessing risks in remote, complex mountainous areas.

Mountain highway networks, characterized by low-redundancy topologies and critical routes traversing geologically active zones, are highly vulnerable to cascading failures triggered by landslides, which can severely disrupt regional connectivity. Currently, there is a lack of universal and efficient methods for accurately identifying, at a regional scale, high-risk landslides that pose substantial threats to highway infrastructure among numerous detected instabilities. To address this gap, this study proposes an integrated pre-disaster risk assessment framework combining interferometric synthetic aperture radar (InSAR), the vector inclination method (VIM), the sloping local base level method (SLBL), and empirical models for the systematic identification and risk prioritization of unstable highway landslides. The framework consists of four core components: wide-area landslide detection, multi-dimensional displacement reconstruction, accurate volume inversion, and run-out distance prediction. Applied in the Jishishan region, the framework identified 530 potential landslides using small baseline subset InSAR (SBAS-InSAR) technology. Among these, 197 were initially determined to potentially threaten highways. By integrating VIM and SLBL methods, landslide volumes were reliably estimated, ranging from 8 × 10³ m³ to 3.3 × 10⁸ m³. Furthermore, six empirical models were employed to rapidly predict potential run-out distances based on landslide volume and topographic parameters, yielding results between 24 m and 2460 m. By comparing these predicted run-out distances with the actual distances to highways, 113 landslides were confirmed to pose realistic threats. Additionally, complex network theory was introduced to evaluate the impact of landslide-induced highway interruptions on regional connectivity. The results show that approximately 17.75% of highway sections in the region fall into "major" or "critical" importance categories, while about 32.74% of the landslides exhibit "major" or "critical" network disruption potential. The failure of such landslides would significantly impair regional transportation functionality, necessitating prioritized risk mitigation and engineering interventions. The proposed non-contact, wide-area applicable risk assessment framework, which provides a scientific basis for precise risk prevention and control in highway systems, is particularly suitable for topographically complex and inaccessible mountainous areas, thereby supporting optimal allocation of disaster mitigation resources.