## 3 Results  
This section shows and comments on the results of the experiments described in Section 2.6. First, U-Net's ability to distinguish between burned and unburned areas will be assessed. Then, the performance achieved to predict the damage severity level by the Double-Step U-Net, the Parallel U-Net and the Simple U-Net will be compared. Furthermore, their scores will be compared with the performance obtained by the dNBR-based solution, the reliable estimator largely adopted in the literature. Finally, the approaches will be discussed analyzing their average prediction error for each severity level. In the binary classification task, the Binary U-Net achieved good performances, as shown in Table 4. According to the F1-Score, the approach was on the average able to classify correctly burned and unburned pixels (sub-areas) for the wildfires in every fold, except for the brown one, which contains data acquired from Sweden. In detail, in that fold, the Binary U-Net showed high Recall, but poor Precision: this means that the network overestimated the burned areas, predicting many false positives. The choice to use the same fold (fucsa, containing data acquired in Spain) as the validation set, helped us to understand the limits of applicability of the approach for data acquired far from the test set. Therefore, training and test areas with strong differences, in terms of geology or land cover aspects, can prevent the neural network from working properly in the binary classification task. However, this limitation is mitigated in the regression problem, discussed below in this section. A detailed performance report for the Single U-Net, the Parallel U-Net, and the Double-Step U-Net in every fold for the estimation of damage severity task is shown Table 5. As mentioned in Section 2.4, the three approaches use solely post-fire satellite acquisition for making their predictions. The table presents the RMSE evaluated on every fold, for every severity level, reported as an ordinal number for the sake of space. Severity levels are mapped as follows; 0 stands for _No damage_, 1 stands for _Negligible to slight damage_, 2 stands for _Moderately Damaged_, 3 stands for _Highly damaged_, and 4 stands for _Completely destroyed_. In a first analysis, we do not consider the dNBR column, but we focus only on the three networks performances. The best score for each row, considering only the U-Net-based approaches, is marked with the star symbol (\(\star\)). Compared to the Single U-Net, the approaches in which the outputs of the Binary U-Net and Regression U-Net are combined showed better overall performances. The Double-Step U-Net is the most accurate in the discrimination between severity levels (1 to 4), achieving best results in 5 folds out of 7 (blue, fucsa, green, orange, and yellow). The only exception is for the brown fold because the Regression U-Net is strongly dependent on the Binary U-Net's performance. However, the RMSE values of the brown fold for the Double-Step U-Net result to be comparable to the RMSE values of other folds (i.e., orange and yellow), and they are better than the ones of the consolidated approach based on dNBR. Therefore, Double-Step U-Net result to be also robust for regions presenting strong differences from the ones that are used to train the model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Bin. U-Net**} & \multicolumn{8}{c}{**Fold**} \\ \cline{2-9}  & **Blue** & **Brown** & **Fucsa** & **Green** & **Orange** & **Red** & **Yellow** \\ \hline Precision & 0.91 & 0.45 & 0.93 & 0.99 & 0.71 & 0.84 & 0.78 \\ Recall & 0.95 & 0.98 & 0.98 & 0.91 & 0.99 & 0.99 & 0.99 \\ F-Score & 0.93 & 0.61 & 0.95 & 0.95 & 0.82 & 0.91 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Binary U-Net cross-validation results for the binary classification subtask. Considering the dNBR in the evaluation, the best performances per row in Table 5 are marked with the dagger symbol (\({}^{\dagger}\)).