Application of UAV photogrammetry technology in identifying discontinuities in slopes in the Pulang copper mine

Identifying discontinuities in high, steep rock slopes is challenging. This study proposes a high-precision geometric feature measurement method for discontinuities on the basis of point cloud data acquired via unmanned aerial vehicle (UAV) photography. The method effectively extracts key parameters, including orientation, trace length, and spacing. The implementation process comprises five main steps. First, principal component analysis (PCA) is used to extract feature information from the point cloud data. Second, the point cloud is preliminarily segmented via a curvature threshold and the density-based spatial clustering with noise (DBSCAN) algorithm. Third, the density peak clustering (DPC) algorithm is adopted to identify cluster centers and divide the discontinuity sets. Fourth, secondary DBSCAN clustering is performed on each discontinuity set to obtain complete individual discontinuities. Finally, geometric characteristics such as orientation, trace length, and spacing are measured on the basis of the principles of analytic geometry. The experimental results show that the orientation deviation calculated by this method is within an acceptable range and that the proposed method has higher computational efficiency than the traditional DPC method. The influence of random fracture networks (DFNs) on the stability of rock slopes was investigated via discrete element numerical simulations.