# CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data
## 4 Implementation and Evaluation of the Method: A Case Study in Boyaca, Colombia
### Features and Labels  
The obtained susceptibility map by geology, geomorphology and land use using the heuristic method for the study area are shown in Figure 4a,b,c, respectively. The landslide susceptibility map obtained using the heuristic method for the study area is shown in Figure 4d. Figure 3: False color NIR, Red and Green composition of the study area using a Sentinel 2A image. The geographical zone covers an area of 300 km\({}^{2}\). The upper left corner of the zone is placed at 4\({}^{\circ}\)54\({}^{\prime}\)44.02\({}^{\prime\prime}\) N, \(-\)73\({}^{\circ}\)21\({}^{\prime}\)22.15\({}^{\prime\prime}\) W.  
Figure 2: Location of the study area corresponding to the department of Boyacá, Colombia. It is important to note that the susceptibility of the map obtained using the heuristic method was defined on a scale of 1-5, with 5 being the highest level of susceptibility. Since the thematic variables selected for the present study involve only information derived from spectral data and DEM data, the performance of the algorithm is challenging in comparison with the results of a heuristic method that has involved morphological, geological and land cover variables with pixel-level accuracy. Taking into account that the scope of the proposed method is to obtain an initial estimate of the landslide susceptibility, it was chosen to reduce these five susceptibility levels to two classes: high susceptibility (for value 5) and moderate susceptibility (for values between 1 and 4). Having said that, and in order to remove light regions that are smaller than a structuring element in the data, the morphological erosion operation was used. This operation converts a given pixel to the minimum pixel value in a neighborhood centered on that pixel. When applying the erosion operation, a flat disk-shaped structuring element with a radius of 1 was used to define the neighborhood (here, a pixel is within the neighborhood if the Euclidean distance between it and the origin is not greater than the radius) [40]. Regarding the feature selection, Figure 5 shows the result of the correlations between the susceptibility value obtained from the heuristic model and the pixel values in each MS band. Figure 4: Inputs and final ground truth map for the study area. (**a**) Map of susceptibility by geology; (**b**) map of susceptibility by geomorphology; (**c**) coverage unit qualification map; (**d**) landslide movement susceptibility map. As shown in Figure 5, the bands have a high degree of positive correlation with each other, while they tend to have a low and negative degree of correlation with the label. Indeed, only three bands (2, 3 and 4) have a correlation higher than 20% (in absolute value) with the susceptibility label. Following this, a feature engineering process was performed using the spectral indices listed in Section 2.2, a DEM was obtained from the Japanese ALOS PALSAR satellite (12.5 m of spatial resolution) and slope data were calculated using ArcGIS Desktop 10.7. It is important to consider here that the original image bands (prior to index calculation) together with the DEM were normalized by dividing by 4095 (\(2^{12}-1\)), while the slope was normalized by dividing by 90 (degrees). Likewise, DEM, slope and the indices were added to the multiband file, resampled to 20 m (nearest neighbor), maintaining the UTM WGS-84 projection. The correlation of the new set of features with each other and the correlation of each variable with the label are shown in Figure 6.  
As depicted in Figure 6, some of the indices have a positive correlation with the output label (Susceptibility, SUSC); likewise, of the twelve indexes evaluated, eight show a correlation equal to or higher than 20% (in absolute value). On the other hand, the DEM has a positive correlation of 28%, while slope has the highest positive correlation (65%). Therefore, DEM and slope are necessary for model training, and some indices could serve as input features in a landslide susceptibility zone classification model.