## 2 Materials and Methods
### Data Acquisition, Preprocessing, and Analysis
More in detail, no correlation is expressed by 0, it is low for values between \(-0.35\) and \(0.35\), and medium to strong for the remaining values [37]. In order to compute the correlation coefficient, a transformation was applied to each image and annotation. Each spectral band, the dNBR, and the EMS severity grading maps (ground truth, GT), all having dimensions \((W\times H)\), have been flattened into a vector of length \((W\times H)\), in order to resemble statistical variables. In the following, we use GT (Ground Truth) to refer to the target variable of this analysis, i.e., the damage severity level. As shown in Figure 2, the spectral bands presenting noticeable correlations (medium or high) with both the dNBR index and the target variable GT (i.e., the damage severity level) are B06, B07, B08, B8A, and B09. Except for B09, the other bands are known in the literature for the computation of the most used indexes for fire detection, such as the Burned Area Index (BAI) [17], the Burned Area Index for Sentinel2 (BAIS2) [38], the Normalized Burn Ratio (NBR) [39], the Normalized Burn Ratio 2 (NBR2) [16], and the Mid-Infrared Burned Index (MIRBI) [40]. Moreover, B07 and B08 are used for the computation of (i) the vegetation index, Normalized Difference Vegetation Index (NDVI) [41], and (ii) the water index, Normalized Difference Water Index (NDWI) [42], respectively. Moreover, it is worth considering the high correlation between dNBR and GT, which is coherent with the literature and confirms the dNBR to be a good estimator of the GT. However, also the Sentinel-2 bands are characterized by high correlation values with respect to the target variable and they can be obtained by considering one single post-fire image, whereas two imagery (pre- and post-fire) are needed to compute the dNBR index. Figure 1: Map of the areas hit by wildfires considered in this study, split by fold. Circles determine the position of the considered wildfires, while each circle colour identifies a specific fold: circles of the same color identify areas of interest attributed to the same fold. In Section 2.4, the new proposed approach, which leverages on Convolutional Neural Networks (CNNs), will be introduced. Given the huge interest from the scientific community in such algorithms, a common practice is to use consolidated approaches by adapting the general methodology to the investigated domain. As most CNNs, the input size is reduced due to computational performances and hardware limitations during the training phase. The high-resolution images retrieved from Sentinel-Hub (and consequently, the grading maps) have dimensions up to 5000 \(\times\) 5000 pixels, but the image size is too big to be processed by a CNN in one shot (due to GPU memory limitations), therefore it needs to be re-adapted. In this paper, we opted for preserving all the provided information by tiling the original acquisitions in smaller crops of size 480 \(\times\) 480 pixels, maintaining the spectral information as provided by Sentinel-Hub. Moreover, only the crops containing at least one pixel classified as burned (a damage severity level between 1 and 4) have been included in the dataset. In the end,  
Figure 3: Severity level distribution among each fold. For each severity level, the percentage of pixels with the considered level is computed within each fold. Figure 2: Correlation Matrix computed on burned areas. Spectral Bands B01–B12 refer to the post-fire Sentinel-2 acquisition, dNBR is the Delta Normalized Burn Ratio, and GT is the severity level target variable (i.e., the Copernicus EMS grading map Ground Truth). the dataset contains a total of 135 crops, distributed in folds as follows; blue fold: 8, brown fold: 9, fucsa fold: 30, green fold: 16, orange fold: 18, red fold: 12, yellow fold: 42. As easily predictable, the dataset's folds present unbalanced EMS damage severity levels, as shown in Figure 3.