## 4 Results  
The point clouds that were generated at both areas were used for further analysis, which was rather common for both datasets, with no restrictions concerning their origin (UAS and TLS, respectively). The structural analysis was carried out using the facets/fracture detection and more specifically following the Kd-tree method [97] of the freeware CloudCompare [98]. The Kd-tree method is a kind of high-dimension binary tree, and by using it, one can search the nearest neighbor points in a relatively quick way [99] based on splitting the space of the mesh into two parts. The top node splits the space in one dimension, and the next nodes split the space in another dimension. The splitting causes approximately half of the points to be stored in the left subtree, and the other half is stored in the right subtree. It stops splitting when the points in one node achieve the given maximum count [99]. In this case study, the Kd-tree method has been used for the extraction of the orientation at each discontinuity located on the slopes under investigation [100; 101]. Since the original data are points, the Kd-tree was used to organize them in k-dimensional space, searching for the neighboring points. Afterward, this point organization is used for dividing the cloud into small planar patches, which are then grouped into facets. The Egremnoi Beach dataset consisted of the final point-cloud, which was generated after the application of the SfM method, followed by the construction of a 3D model using Agisoft Metashape software in mesh format (i.e., Triangular irregular network--TIN) with almost 25 million facets, after excluding large parts of the slope that were covered by vegetation, which altered the slope morphology (Figure 7a). The vegetation removal succeeded after classifying the points of the cloud, due to relatively high values of green in the Red-Green-Blue color model. The resulting dataset was used at the next stage for the structural analysis of the steep slopes along the coastline, considering that a large accumulation of debris occurred at certain locations next to the slope's prone base. The dip of every facet was calculated by using CloudCompare [98] algorithms (Figure 7b), and the patterns that were identified at the dip profile map proved to be more than helpful for the prone segmentation, as it was quite large for further processing. Therefore, it was divided into three segments and the discontinuity planes were statistically interpreted individually (Figure 7c). The average geometric characteristics of the topography of the northern segment are 55/275, and the orientation of 4141 discontinuity planes that were extracted from the point-cloud processing was concentrated in two main groups at 24/289 and 43/237. The kinematic analysis of the discontinuity poles using Dips software v.7.0 [102], in the context of the wedge-sliding risk, indicated that 29% of the facet pairings are under serious rockfall potential, considering that the friction angle is set at 30\({}^{\circ}\)[87; 103]. The 3259 facets identified at the southern segment are concentrated more or less in the same orientations (42/302 and 43/246) and the morphology is approximately identical (55/273). The kinematic analysis for wedge sliding showed that 25% of the discontinuity pairings could lead to failure, especially after an earthquake occurrence. The respective percentage of potential rockfall at the median segment of the Egremnoi cliff is lower (23%), even though the statistical significance of one of the main facet groups was dramatically decreased and substituted from a new, almost vertical one, with E-W trending (87/358). The latter is the result of the kinematic analysis of 4058 discontinuity planes and the second pole concentration is at 39/301, which is very similar to those concentrations identified at the other two segments' interpretations.