# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 5 Geographic Information Interpretation and Analysis  
### Identification of Geological Hazard Prone Areas  
The contour map, generated based on the Digital Elevation Model (Figure 7), visually represents the surface undulation pattern of the study area. The map indicates significant elevation variation in the study area, ranging from 192 m to 383 m, with numerous cliffs and steep slopes present. A comparison of the contour maps from both time periods (before-and-after aerial photography) reveals a more pronounced contour overlap in the area highlighted by the red box. Since the flight status parameters remained unchanged between the two aerial photographs, it can be qualitatively concluded that the steepness of this section of the area has changed, and there is a high probability that the slope pattern of the mountain has been altered. Figure 6: Digital Elevation Model. Figure 7: Contour map: (**a**) Phase I contour map; (**b**) Phase II contour map. The pre-processed point-cloud models from both phases were aligned in the same coordinate system for subsequent analysis. The Iterative Closest Point (ICP) method was used for alignment, selecting the first phase of the point-cloud as the reference datum. The core of the ICP algorithm is to minimize the distance error between two sets of point clouds by iteratively optimizing the transformation matrix (rotation + translation) [39]. The alignment of the second phase was performed with the root-mean-square error (RMSE) mode as the termination criterion, set to \(1\times 10^{-5}\)[40; 41]. RMSE directly reflects alignment accuracy and serves as a key condition for iteration termination. When the change in RMSE between two consecutive iterations falls below a specified threshold, the algorithm halts, and the alignment is considered to have converged. The number of alignment samples was set to 50,000. The LiDAR360 software's change detection plugin was used to compare the point-cloud models of the two aerial photographs, reflecting the changes in the region between the two periods. The grid edge length for the point-cloud was set to 2, with an elevation tolerance of 0.5 dm and a change distance of 0, generating the differential point-cloud model of the study area (Figure 8). The green dots represent the elevation changes in the comparison point-cloud (Phase II) relative to the reference point-cloud (Phase I). Three areas with significant elevation changes were identified in the study area, as shown in the red boxes in the figure, which are prone to geological hazards such as landslides and rockfalls. These areas should be closely monitored in the future. Three hazardous areas were identified in the study area through a comprehensive analysis of the 3D models, contour model, and point-cloud differential model (Figure 9). Based on field investigation and close photographic observation, the mountain restoration project in Area 2 involved road construction, and the elevation change is clearly due to the accumulation of soil along both sides of the road, eliminating the potential risk. In Area 1, based on the contour maps mentioned above and field investigation, multi-story stepped cliffs are distributed in this area, and the mountain slopes show severe weathering and fragmentation. Minor rockfall events have already occurred in this area. Additionally, with the entry of heavy machinery for the mountain restoration project, small-scale rock collapses occur intermittently. Area 3 has been affected by heavy machinery, weathering, and freeze-thaw cycles over an extended period, resulting in fragmented rock and steep mountain slopes. Small-scale geotechnical slides have occurred in this area. These conditions make the area highly susceptible to geological disasters, threatening the stability of the slope and the safety of highway traffic below. Figure 8: Point-cloud difference model. In summary, attention must be given to Areas 1 and 3.