Optimized air-ground data fusion method for mine slope modeling

Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards. Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes, this study introduces a new method that fuses model data from Unmanned aerial vehicles (UAV) tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points. First, the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning. Then, the elbow rule is applied to determine the optimal cluster number (Ko), and the feature points are extracted. Next, the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry, and the internal point coordinates are adjusted through the distance-weighted average to construct a 3D model. Finally, by integrating an engineering case study, the Ko value is determined to be 8, with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm. Therefore, compared with the modeling method utilizing K-medoids clustering algorithm, the new modeling method significantly enhances the computational efficiency, the accuracy of selecting the optimal number of feature points in 3D laser scanning, and the precision of the 3D model derived from UAV tilt photogrammetry. This method provides a research foundation for constructing mine slope model.