## 4 Results and Discussion
### Results of Landslide Detection  
Classification techniques affect the quality of the classification maps. Many classification algorithms have been established for each category, and each has its merits and demerits. In the present work, the RNN and MLP-NN models with optimized parameters were used for landslide detection with good accuracy. Figure 9 shows the classification results of the RNN and MLP-NN models in the analysis area. The qualitative assessment of the RNN model yielded high-quality results, as shown in Figure 9A. Well-defined landslide boundaries were detected and correctly differentiated from other objects (cut-slope, bare soil and vegetation). On the other hand, the qualitative assessment of MLP-NN produced low-quality results, as shown in Figure 9B. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Initial Parameters**} & \multicolumn{4}{c}{**Optimal Parameters**} \\ \hline Number & Scale & Shape & Compactness & Scale & Shape & Compactness \\
1 & 50 & 0.1 & 0.1 & 75.52 & 0.4 & 0.5 \\
2 & 80 & 0.1 & 0.1 & 100 & 0.45 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multi-resolution segmentation parameters. Figure 8: Parameter optimization of the multiresolution segmentation algorithm: (**a**) initial segmentation and (**b**) optimized segmentation. The proposed models were evaluated using another LiDAR dataset (test site) from the Cameron Highlands. All features (all existing objects) of the test area were carefully considered. Segmentation parameters were optimized using the FbSP optimizer. A 10-fold cross-validation approach introduced by [PERSON] et al. [53] was used to resolve this issue with high accuracy. Environmental conditions and differences in landslide characteristics resulted in misclassification [9]. Differences in the sensors used, illumination conditions, and the spatial resolutions of images are some of the challenges faced by the proposed NN models [54]. The results of qualitative assessment indicated that the proposed NNs with optimized techniques correctly detected landslide locations in the test site, as shown in Figure 10. The qualitative assessment of the RNN model yielded high-quality results, as shown in Figure 10A,B. On the other hand, the qualitative assessment of the MLP-NN model produced low-quality results, as shown in Figure 10.  
Figure 10: Results of the qualitative assessment of (**A**) RNN and (**B**) MLP-NN for the test area. Figure 9: Results of the qualitative assessment of (**A**) RNN and (**B**) MLP-NN for the analysis area. It is crucial to take the required measures to avoid the issue of the landslide separation from the bare land. The morphology characteristics of the landslide map is different from other types of land cover. For example, the shape, slope and other characteristics (i.e., dip direction, width and length) of the surface terrain may be changed after landslide occurs. Therefore, by using relevant features derived from very high resolution LiDAR, data such as texture and geometric features can be used to separate between landslides and bare land. In addition, applying different optimization techniques helped us to improve the classification accuracy in landslide detection over other landcover classes, such as bare land, man-made, etc., as described previously by [PERSON] and [PERSON] [9]. Their results demonstrated that using optimized techniques with very high resolution LiDAR data (0.5) enabled them to separate landslide and other types of land cover. In addition, the most relevant features in Table 4 were optimized during this study. Furthermore, authors of [16] suggested that using the object feature from LiDAR data is a suitable solution for landslide identification. The landslide detection results showed that the proposed model is robust. Optimizing the segmentation parameters, namely, scale, shape, and compactness, using the fuzzy logic supervised approach resulted in the effective differentiation of landslide from non-landslide (bare soil, cut slope and vegetation) objects.