# UAV-Based Evaluation of Rockfall Hazard in the Cultural Heritage Area of Kipinas Monastery, Greece
## 2 Methods and Data
### Development of the 3D Model  
Considering the unique location of the monastery and the steep morphology of the rock slope, it was decided to conduct a photogrammetry survey via a manually flown UAV in September of 2020, in order to construct an accurate and detailed 3D model of the investigated site.  
The camera view direction for the UAV-based photogrammetric survey was implemented as [64] proposed for steep slopes. In [64], it is suggested that for steep slopes (slope angle \(>40^{\circ}\), usually rock slopes), an oblique or even horizontal camera view should be applied in order to achieve the optimal results during the acquisition of the imagery data.  
The UAV gear, via which the imagery data were collected, was a DJI Phantom 4 Pro V2.0 equipped with a 1-inch 20-megapixel sensor and an FC6310 camera of 8.8 mm focal length and a manually adjustable aperture from F2.8 to F11. A total of 362 images were acquired, in order to cover the whole slope face. The derived images from the UAV were processed using the Pix4 Dmapper software v. 4.5.6. (Pix4D, Lausanne, Switzerland), resulting in a dense 3D point cloud dataset consisting of about 104 million points with a ground resolution of 1.27 cm/pixel and a reprojection error of 0.157 pixels. Further processing of the data enabled the construction of a detailed DEM (Digital Elevation Model) with the same pixel dimensions as the point cloud. The generated point cloud was georeferenced using the UAV sensor GPS data and a set of 5 artificial GCP (Ground Control Points) according to the methodology proposed by [41; 65]. Considering the steep  
Figure 2: Stereographic projections of the 122 field–obtained tectonic measurements.  
geometry of the slope and the relevant hazard to access higher parts, the GCP were placed in accessible lower parts across the road that leads to the monastery in some distinctive areas, as shown in Figure 3.  
Following the extraction, the point cloud was processed using the CloudCompareTM software v.2.11.1 (Anoia). In order to proceed to the structural analysis, the whole 3D model was trimmed of vegetation, constructions and \"noise\" in general. This prevented their presence in the final point cloud from affecting the correct recognition of discontinuities.  
Then, the point cloud was segmented into four (4) different sub-regions depending on two (2) main criteria. The sub-regions were divided in order for each to preserve a consistent dip direction at the face of the slope and their downstream area to sustain a similar impact level, in the potential event of rockfall. This segmentation of the 3D point cloud into the 4 sub-regions is shown in Figure 4. The main characteristics of each area that the initial point cloud was segmented into are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-Region** & **Area Dimension (m2)** & **Slope Dip/Dip Direction** \\ \hline U1 & 2427 & 87/157 \\ \hline U2 & 3152 & 85/130 \\ \hline U3 & 3594 & 87/315 \\ \hline U4 & 909 & 87/120 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of the sub-regions that the initial point cloud was segmented into.  
Figure 3: The constructed 3D point cloud model with used GCPs shown as yellow dots.