## 2 Materials and Methods
### Data Acquisition, Preprocessing, and Analysis  
As introduced in the previous section, Copernicus EMS provides grading maps indicating the severity of damage caused by wildfires. They refer to (i) an Area of Interest (AoI) and (ii) a reference date (marked as \"Situation as of\" in the map's cartouche). The AoI is a squared region which includes the area/s hit by the wildfire. It is composed of two tuples of coordinates <_Longitude, Latitude>_, which indicate the top-left and bottom-right edges of the region. The reference date is the post-wildfire date used as a reference for the analysis by the domain experts. Sentinel-2 data was downloaded referring to both the AoI and the reference date specified in the EMS grading map. However, it is quite common that, under those constraints, the Sentinel-2 data of the area of interest could not be available. Commonly, the reason can be twofold: (i) AoI was not explored or partially explored by satellites in the reference date, or (ii) the AoI is mostly covered with clouds. Therefore, to be used in this paper, Sentinel-2 acquisitions were subjected to three constraints: (i) the satellite acquisition must be equal to the reference date, (ii) data must be available for at least the 90% of the AoI, and (iii) cloud coverage must not exceed the 10% of the AoI. While the data availability is given by the Sentinel-Hub service, the cloud coverage value was estimated according to the method proposed by [PERSON], [PERSON] and [PERSON] [36]. In this paper, 21 Copernicus EMS grading maps have been collected from 5 European regions: Portugal, Spain, France, Italy, and Sweden. The Sentinel-2 data have been downloaded at the highest resolution. Then, the AoIs were split into 7 folds, according to two different constraints: (i) a fold must include at least two AoIs, and (ii) areas of interest must be geographically close. A representation of the geographical distribution of the wildfire-affected regions and their categorization in folds is shown in Figure 1.  
Once downloaded, satellite acquisitions are images of dimensions \((W\times H\times D)\). \(W\) and \(H\) are the acquisition Width and Height, respectively, and are up to \(5000\times 5000\) pixels. \(D\), the Depth, is the number of spectral bands, which is 12 for Sentinel-2 L2A imagery. Copernicus EMS grading maps are images of size \(W\times H\), having the same dimensions of the satellite acquisitions. Each pixel of the Copernicus annotation determines the damage severity level of the corresponding pixel--at the same row and column--of the Sentinel-2 acquisition. Details about the collection, especially the Copernicus EMS annotations, the dates in which Sentinel-2 data were acquired, and the fold they were assigned to are reported in Appendix A. Moreover, in order to allow the proposed method to generalize among different kinds of vegetation, another aspect considered in the dataset is the heterogeneity of land use, which includes inland areas with dense vegetation (i.e., red fold), areas characterized by cropland and small or sparse trees (i.e., fucsa fold), coastal areas (i.e., blue fold) and rural areas with little or no vegetation (i.e., yellow fold). A detail of the land use distribution for every AoI included in the dataset is reported in the Appendix B. In order to assess the spectral bands that can provide useful information for the detection of the damage severity, the Pearson's correlation between the bands of the collected AoIs, the dNBR, and the ground truth EMS damage level have been investigated. We considered also the dNBR index because it is widely used in previous papers and we want to understand if that index, based on pre- and post-imagery, is more correlated to the target variable than the Sentinel-2 bands. The Pearson's correlation is a measure computed between two variables and it ranges between \(-1\) and \(1\). High correlation is determined at the extremes of its domain: a highly positive or a highly negative value means the two variables are directly or indirectly related, respectively.