## 1 Introduction  
Landslides represent a common and highly destructive category of geological hazards, characterized by frequent occurrences and extensive impacts, thus becoming a critical focus in disaster prevention and mitigation [1]. Landslide hazards specifically refer to slopes with imminent potential for destabilization, resulting in disasters. Not all landslides, however, qualify as hazards; this designation applies only to landslides that threaten or cause damage to lives and property [2]. Notable landslide events, such as the catastrophic incident in Shuicheng, Guizhou Province, on 23 July 2019, resulting in 43 fatalities and nine missing persons [3], and another severe event in Liangshui Village, Tangfang Town, Zhenxiong County, Yunnan Province,on 22 January 2024, which caused 44 deaths [4], underscore the strong need for effective geological disaster prevention. Responding to escalating landslide threats, regions across China have systematically conducted hazard assessments and risk evaluations. [PERSON] and colleagues proposed an integrated \"Heaven-Air-Ground\" three-tier monitoring system for the early detection of significant geological hazards, which has achieved national implementation and which has yielded notable results [5]. [PERSON] and others proposed a landslide-susceptibility prediction approach integrating optimized sampling with stacking ensemble learning, which significantly improved the quality of negative samples and which achieved a high prediction accuracy of 97.1%, offering an effective technical framework for regional landslide risk assessment [6]. Nevertheless, around 80% of catastrophic geological disasters still occur at unidentified hazard sites, indicating various undiscovered threats [2]. Consequently, the early detection and monitoring of landslide hazards remain pivotal and challenging areas in landslide disaster research. Landslide hazards commonly occur in remote, upper slope areas characterized by complex terrain and dense vegetation cover, rendering traditional manual inspections and community-based monitoring inadequate for extensive, high-risk area evaluations [7]. Developing proactive detection methods, precise hazard identification techniques, and intelligent, systematic monitoring and early-warning systems constitutes a critical scientific challenge in landslide disaster mitigation. This emphasizes the necessity of transitioning from passive, post-disaster responses to proactive, pre-disaster identification and intervention strategies. However, existing monitoring methods, constrained by spatial coverage limitations and intensive human-resource requirements, are typically effective only in small-scale, localized scenarios, making continuous and regional-scale hazard identification and dynamic updating difficult to achieve [8; 9]. Remote-sensing technology, notable for its high timeliness, spatial resolution, and broad coverage capabilities, provides innovative solutions for macro-scale landslide monitoring. Optical remote-sensing imagery excels in surface-feature extraction and has been widely applied in geological hazard assessments and environmental monitoring. However, optical imagery is frequently impeded by cloud coverage, variable lighting conditions, and meteorological factors, particularly limiting its efficacy in detecting subtle ground deformations [10]. In contrast, spaceborne Synthetic Aperture Radar (SAR) remote-sensing technology, capable of all-weather and day-night imaging, penetrates cloud coverage and vegetation effectively to precisely monitor ground deformation. Utilizing Interferometric SAR (InSAR), millimeter-level deformation can be observed, effectively detecting subtle movements indicative of potential landslide activities [11]. Consequently, SAR remote-sensing technology demonstrates significant potential and has become a critical tool in the early identification and continuous monitoring of landslide hazards.