# CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data
## 2 Data Preparation
### Feature Selection from MS Data  
The thematic variables used in previous studies can be classified into five categories: morphological, geological, land cover, hydrological and other variables. The variables related to terrain morphology are usually obtained by processing terrain elevation data (e.g., DEM) and are the most commonly used variables since they have proven to be effective in predicting the spatial distribution of landslides [17].  
From the geological point of view, one of the most common types of information is related to rock type. In the case of land cover and land use, this type of data can be complemented with results from the processing of optical satellite imagery. In addition, variables that measure physical characteristics related to, for example, vegetation cover have also been included in studies due to the ease of calculation for large areas using satellite images [32]. Other types of variables that have been used are related to geo-environmental information and other data. Distance to faults is one of them, but its disadvantage is that it is not information that is always available, whereas distance to rivers is mainly related to hydrologically controlled events [17].  
According to [17], new methods based on remote sensing information captured by sensors such as Landsat or Sentinel-2 satellites are expected to contribute to the solution of the landslide susceptibility modeling problem. In fact, the idea in the current research is to evaluate the use of multispectral images as the main starting information for the landslide susceptibility model, given their wide availability at different scales that would allow feeding landslide early warning systems. Nevertheless, it is also necessary to evaluate the possibility of adding other variables beyond such multispectral data. In order to select the features that will be used in the current research, two alternatives can be considered.  
The first is to perform a feature engineering process to transform the data into potentially more meaningful representations based on domain knowledge (i.e., to combine two or more features to create new ones). This can be achieved by arithmetic operations of the numerical characteristics (spectral indices) that are applied to the different spectral bands of an image per pixel. These indices allow highlighting the characteristics of the area, such as vegetation, humidity, water, bare soil, etc., which could be important when estimating the susceptibility of an area to landslides. Therefore, the use of consolidated indices in the literature allows adding domain knowledge to the dataset. Some spectral indices for feature engineering are listed below, and the specific formula of each index for several sensors can be consulted in [33].  
* Normalized Difference Vegetation Index (NDVI)
* Soil Adjusted Vegetation Index (SAVI)
* Normalized Difference Moisture Index (NDMI)
* Moisture Stress Index (MSI)
* Bare Soil Index (BSI)
* Soil Brightness Index (SBI)
* Alteration (ALT)
* Ferrous Silicates (SF)
* Normalized Burned Ratio Index (NBRI)
* Normalized Difference Water Index (NDWI)
* Normalized Difference Snow Index (NDSI)
* Normalized Difference Glacier Index (NDGI)  
The second involves the use of complementary pixel-level data such as DEM (as suggested in [17]) and the corresponding rate of change in elevation for DEM since they constitute important inputs to obtain the variables that compose the geometric characteristics of the slope.  
Finally, it is also important to evaluate the contribution that each of the input features can have to a classifier. To obtain this contribution, the decrease in the impurity of each feature can be averaged, where the impurity is the measure on which the choice of the (locally) optimal condition of a node in a decision tree is based.