# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 4 Remote Sensing Image Data Processing
### Point-Cloud Data Processing
#### 4.2.1 Point-Cloud Data Pre-Processing  
Denoising of point-cloud data [33]. When using a UAV aerial photography system to collect image data of the study area, noise often occurs due to interference from objects in the environment, which can affect the accuracy of the point-cloud model. The standard deviation multiplier is set as mean \(K\), and the software searches for neighboring points for \begin{table}
\begin{tabular}{c c c} \hline
**Parameter Classification** & **Parameter Name** & **Parametric Indicators** \\ \hline \multirow{4}{*}{Image Information} & Number of images & 74 \\  & With positional image & 74 \\  & Calibrated images & 74 \\  & Root-mean-square error of & Phase I 0.957 m; \\  & geographic alignment & Phase II 1.364 m \\ \hline \multirow{2}{*}{times} & airtime & Phase I 37 s; Phase II 53 s \\ \hline \multirow{4}{*}{RTK solution value} & fixed solution & Phase I 74; Phase II 74 \\  & floating solution & Phase I 0; Phase II 0 \\ \cline{1-1}  & single-point solution & Phase I 0; Phase II 0 \\ \cline{1-1}  & Other solutions & Phase I 0; Phase II 0 \\ \hline \end{tabular}
\end{table} 
Table 2: Aerial triangulation for quality assessment report. Figure 4: 3D reconstruction of mining area: (**a**) Phase I point-cloud model; (**b**) Phase II point-cloud model. Figure 3: 3D model: (**a**) Phase I 3D model; (**b**) Phase II 3D model. each point, specifying the number of neighboring points. It then calculates the average distance between the point and its neighbors using \(D\) as a surrogate and computes the median distance (mean \(D\)) and the standard deviation (S). If the maximum distance (Max \(D\)) exceeds the threshold (Max \(D\) = mean \(D\) + mean \(K\) * \(S\)), noisy points are identified and removed. The smaller the value of S, the more points are classified as noise. The standard deviation multiplier and the number of neighboring points are set to 5 and 10, respectively. Through statistical significance, sensitivity analysis, and engineering validation, it is demonstrated that the combination of a standard deviation multiplier \(K\) = 5 and a neighborhood point count \(n\) = 10 achieves an optimal balance between denoising rate, false deletion rate, and terrain fidelity. This parameter setting is particularly suitable for medium- to high-density LiDAR point clouds [34; 35]. The denoising process is then completed for both sets of point-cloud models. The point-cloud density is approximately 22 points/m\({}^{2}\). The ground point classification process is performed on the point-cloud model of the study area using the Improved Progressive TIN Densification (IPTD) algorithm. This algorithm initially generates a sparse triangular mesh from seed points and then iteratively refines the mesh layer by layer until all the ground points are classified. The largest building in the region is a square pump house located in the southeast of the mountain, with dimensions of 15 m \(\times\) 15 m \(\times\) 6 m. Therefore, the maximum building size on the horizontal plane is set to 15 m. The terrain in the region has significant undulation, and thus the iteration angle is set to 15\({}^{\circ}\) (greater than the default of 8\({}^{\circ}\)). The default iteration angle of 8\({}^{\circ}\) may not accurately capture the true ground morphology in steep slope areas, leading to the misclassification of ground points as non-ground points (due to local slopes exceeding the threshold), which in turn causes a fragmented terrain model. Additionally, the default iteration distance of 1.4 m (the maximum distance between a point and the apex of the triangulation plane) may prevent the triangulation mesh from expanding in areas with abrupt terrain changes (e.g., cliffs, steep anyons), resulting in voids. To address this, increasing the iteration distance to 2 m enhances the continuity of the triangulation mesh in fragmented terrain and ensures efficient connection of neighboring ground points [34; 35; 36], while maintaining other parameters at the default level. The parameters for both phases of the model are set the same.