## 1 Introduction
For example, solid-state LiDAR, when combined with detection algorithms, enables real-time identification of small-scale rockfalls in complex terrains [14]. In addition, airborne LiDAR integrated with multi-source imagery significantly improves the visualization and automated detection accuracy of landslidehazards in mountainous regions [15]. [PERSON] generated a high-resolution DEM from airborne LiDAR point clouds and integrated terrain shading with multisource imagery, significantly improving the visualization and automatic detection of landslide hazards in complex, vegetated terrain. This approach provides an efficient and reliable method for conducting large-scale landslide inventories [16]. Accurate prediction of rockfall trajectories and impact zones depends on numerical simulations. Traditional two-dimensional numerical models, limited by geometric simplifications, can only simulate rockfall free-fall and rebound within a single cross-section. As a result, they fail to capture the three-dimensional dispersion and multiple impacts of rock blocks in complex terrain [17; 18; 19]. Although models such as CRSP (Cluster-based Rockfall Susceptibility Prediction) and Rockyfor3D are widely used, the Swiss RAMMS:ROCKFALL model provides enhanced capabilities for simulating the full rockfall process--including jumping, rolling, and collision--on realistic 3D terrains. Compared to CRSP and Rockyfor3D, RAMMS::ROCKFALL incorporates a GPU-accelerated particle-rigid body coupled solver, unstructured meshes integrated with centimeter-resolution DEMs, and specialized models for collision fragmentation and energy dissipation in high-energy block impacts. It is well suited for high-precision simulations in complex environments such as canyon terrains, residential zones, and transportation infrastructure. Domestic researchers have used this model to perform three-dimensional kinematic analyses of earthquake-induced collapses, revealing energy distribution patterns [20], and to derive bounce heights, velocities, and kinetic energies under real terrain conditions for delineating potential hazard zones [21]. However, a systematic integration of high-precision 3D mapping with dynamic simulation remains lacking--especially in parameter calibration and protective design for coupled scenarios involving mountainous residential zones and transportation routes. To address this gap, the present study focused on the representative Lujiawan resettlement site. UAV (unmanned aerial vehicle)-derived point clouds, combined with stereographic projection methods, were first used to accurately identify structural planes and potential instability modes [22; 23]. Then, three-dimensional dynamic simulations were performed using the RAMMS::ROCKFALL platform to quantitatively predict rockfall trajectories, kinetic energies, and accumulation patterns [24]. Building on existing technologies, this study developed a closed-loop quantitative workflow. First, high-resolution 3D laser scanning was conducted using centimeter-level UAV LiDAR (unmanned aerial vehicle-borne LiDAR) to reconstruct detailed geometric models of hazardous rock masses. Next, UAV imagery and field survey data were integrated to extract structural plane parameters such as joint orientations, and stereographic projection was applied to rapidly identify potential failure modes, including planar sliding, wedge sliding, and toppling. Based on this analysis, the limit equilibrium method was used to calculate safety factors for refined stability assessments. Finally, the evaluation results were imported into the RAMMS::ROCKFALL platform to conduct 3D dynamic simulations, generating key indicators such as rockfall trajectories, kinetic energies, bounce heights, and velocities. This workflow enables a systematic and repeatable framework for geometric reconstruction, structural analysis, and collapse risk prediction of hazardous rock masses.