## 1 Introduction
In literature, damage severity caused by wildfires is commonly determined through two different indices, depending on whether the data are acquired manually, through ground inspection [23], or from remote sensors (UAV/satellites) [24]. The Composite Burned Index (CBI) [23] is computed by considering manually collected data. Specifically, this first index is computed by considering detailed aspects, such as the condition and colour of the soil, the amount of vegetation or fuel consumed, the resprouting from burned plants, the establishment of new colonizing species, and blackening or scorching of trees. All those aspects are combined to create the Composite Burned Index (CBI), which is the best approximation of damage severity, but it is highly expensive to obtain, especially for large regions because it is based on manually acquired data. This damage severity index is even more fine-grained than the one provided by EMS, but its computation is not feasible at a large scale because too many data must be collected manually. From remotely sensed imagery, the combination of spectral wavelengths of light particularly susceptible to water, vegetation, and inert materials make the identification of fires easier, through suitable indices like the Normalized Burn Ratio (NBR) [24]. The difference of NBR values computed on acquisitions of the same area at different times (pre- and post-wildfire), allows estimating variations of the soil caused by events like a wildfire. This kind of difference, named _delta Normalized Burnt Ratio_ (dNBR) [22], is known in the literature as a valid estimator/index of the severity of burned regions. However, thresholds used for determining severity levels may vary among acquisitions and may be soil-dependent. Differently from this approach, our technique uses only a post-fire image. Hence, our approach is not affected by the selection of a pre-wildfire image taken in a condition comparable to that of the post-wildfire image. In their work [22], [PERSON] and [PERSON] specify that the imagery used in their study were chosen such that pre- and post-wildfire dates were as close to anniversary dates as possible to minimize differences in phenology. This \"alignment\" operation,and other preprocessing operations, are not needed when our approach is used because only the post-wildfire image is needed. A recent work of [PERSON] et al. [25] studied the relatedness between the dNBR computed from Landsat-8 and Sentinel-2 satellites acquisitions with the CBI of an area affected by fire in Italy. The dNBR computed from Sentinel-2 acquisitions resulted to be a valid estimator of the CBI and therefore very useful for the estimation of severity levels. However, [PERSON] et al. [26] adjusted the thresholds of both dNBR and the _delta Normalized Difference Vegetation Index_ (dNDVI) values to evaluate the severity of two wildfires occurred in Patagonia between December 2013 and January 2014. Like the dNBR, the dNDVI is computed as the temporal difference of two indices--NDVI pre-fire and NDVI post-fire--sensible to the presence of vegetation. The work proves that, under an accurate fine-tuning, those indices are valid estimators of the more accurate CBI. However, the complex (non-automatic) fine-tuning of a set of thresholds is needed. Moreover, [PERSON] et al. [27] evaluated the damage severity of a single wildfire ignited in the Great Hinggan Mountains (China) using fine-tuned thresholds on NBR and dNBR. Moreover, in that case, the time-consuming and complex fine-tuning operation is needed to obtain good results. Other works imply the adoption of machine learning techniques. [PERSON] et al. [28] proposed a transfer learning approach based on a Support Vector Regressor (SVR) to predict the CBI index value, which outperforms the aforementioned approaches. However, the technique proposed by [PERSON] et al. was validated on pre- and post-fire satellite acquisitions on a unique fire event occurred in the Southwest of United States, in 2002 and therefore its generality has not been validated.