## 4 Conclusions  
This work introduces the application of a convolutional neural network, namely, U-Net for the estimation of the damage severity of regions affected by wildfires from satellite imagery. Compared to the literature, which commonly uses pre-fire and post-fire satellite acquisitions, this approach only makes use of post-fire data. Moreover, our approach results to be location-independent for the assessed European regions, and possibly for all the areas presenting common land-use as the AoIs presented in this work), being able to process geographically distributed satellite imagery. Furthermore, a modified version of U-Net named Double-Step U-Net, is created and introduced to improve the performance of the standard method. The approaches have been validated across five European regions, within 21 manually annotated wildfire events. As a result, Double-Step U-Net outperformed U-Net and achieved comparable performance to the thresholded dNBR, which is computed using both pre- and post-wildfire satellite acquisitions.  
Future works will assess the contribution of other information, like Synthetic-aperture radar (SAR) data provided by Sentinel-1 data, or the Digital Elevation Maps (DEM) to improve the performances of Double-Step U-Net for distinguishing between wildfire severity levels. Moreover, the approach can be adapted and evolved to analyse the daily wildfire expansion, in order to provide a risk map for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Severity**} & \multicolumn{4}{c}{**Overall Per-Class Performance (RMSE)**} \\ \cline{2-5}  & **dNBR** & **Single U-Net** & **Parallel U-Net** & **Double-Step U-Net** \\ \hline
0 & 0.62 & 0.42 & 0.20 \({}^{\star}\) & 0.35 \\
1 & 0.95 \({}^{\ddagger}\) & 1.07 & 1.05 & 1.03 \({}^{\star}\) \\
2 & 1.09 & 1.01 & 1.02 & 0.94 \({}^{\star}\)\({}^{\star}\) \\
3 & 1.02 & 0.95 & 0.97 & 0.76 \({}^{\star}\)\({}^{\ddagger}\) \\
4 & 0.91 \({}^{\ddagger}\) & 1.45 & 1.46 & 1.30 \({}^{\star}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average performance for severity level. (\({}^{\star}\)) indicates the best RMSE per severity category among the three U-Net versions. (\({}^{\dagger}\)) indicates the best RMSE per severity category, dNBR included.  
the potential areas that could be affected in the near future. Therefore, areas more likely to be affected by severe damage can be identified earlier, in order to let decision-makers to conduct better disaster response management and to limit overall damages.  
Figure 6: Wildfire severity level predictions. The severity levels range from black (severity 0) to white (severity 4). (**a1,b1**) Sentinel L2A acquisition; (**a2,b2**) Copernicus EMS grading map (GT); (**a3,b3**) Binary mask generated by the Binary U-Net: black and white colors indicate unburned and burned regions, respectively; (**a4,b4**) Thresholded MNBR, obtained from pre and post-fire acquisitions; (**a5,b5**) Single U-Net prediction; (**a6,b6**) Double-Step U-Net prediction.  
**Author Contributions:** Conceptualization, A.F. and L.C.; methodology, A.F. and L.C.; software, L.C. and A.F.; validation, A.F., L.C. and P.G.; formal analysis, A.F., L.C., and P.G.; investigation, A.F. and L.C.; resources, A.F. and L.C.; data curation, L.C. and A.F.; writing-original draft preparation, A.F., L.C. and P.G.; writing-review and editing, P.G.; visualization, A.F. and L.C.; supervision, P.G.; funding acquisition, A.F. and P.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by the European Commission through the SHELTER project, grant agreement n.821282. The APC was funded by Politecnico di Torino.  
**Acknowledgments:** Data used in this work is provided by the European Space Agency (ESA). The authors thank [PERSON] (Sentinel-Hub) and [PERSON] (ESA) for their prompt support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.