## 5 Conclusions  
In the past decades, the number of wildfires has increased while the available material and human capacities to fight them are still limited. Hence the response capacity must be optimized to avoid time and logistic loss due to false alarms. Various intelligent systems, namely artificial intelligence models, concepted to detect and localize fire zones are proposed. Few tendencies to work over aerial datasets make the segmentation process challenging due to the lack of fire pixels. The current system draws very encouraging results over a minor set of aerial fire images. To conclude, the selected model trained with ResNet-50 and Dice loss attains a global accuracy of 98.70%, a mean accuracy of 89.54%, a mean IoU 86.38%, a weighted IoU of 97.51%, and a mean BF score of 93.86%. The computational cost is moderated. Nevertheless, the trained model could be used for segmentation of more similar aerial pictures that the manual segmentation is challenging and time consuming since it requires an affine level of precision. The used aerial images are considered as limited set of data, since the number of pixels labeled as fire are less than usual. Hence, we had reinforced the dataset with Corsican pictures that have a frontal view of fire. The Firefront_Gestosa is private limited content. A performance analysis as functions of the training samples ratio could be conducted, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Approach** & **Dataset** & **Global Accuracy** & **Mean Accuracy (Recall)** & **Mean IoU** & **Weighted IoU** & **Mean BF Score (F1 Score)** \\ \hline
[11] & UNET & 99\% & 83.88\% & 78.17\% & — & 87.75\% \\ \hline
**Proposed** &
\begin{tabular}{c} **Deeplabv3+ with** \\ **ResNet-50 backbone and** \\ **Dice loss function** \\ \end{tabular} & **FLAME** & **99.79\%** & **92.02\%** & **84.74\%** & **99.61\%** & **96.43\%** \\ \hline
[49] &
\begin{tabular}{c} Custom CNN \\ residual network \\ \end{tabular} & 97.46\% & 95.17\% & 90.02\% & — & 94.70\% \\ \hline
[48] & Deep–Fire U-Net & 94.39\% & 88.78\% & 82.32\% & — & 89.48\% \\ \hline
[36] & Deeplab & 96.92\% & 90.42\% & 86.96\% & — & 92.69\% \\ \hline
[51] &
\begin{tabular}{c} Color segmentation with \\ fuzzy criteria \\ \end{tabular} & **Corsican** & 92.74\% & 75.10\% & 72.53\% & — & 80.04\% \\ \hline
[24] &
\begin{tabular}{c} bee colony algorithm-based \\ color space segmentation \\ \end{tabular} & — & — & 76\% & — & — \\ \hline
[50] & Custom CNN architecture & 98.02\% & — & 92.53\% & — \\ \hline
[52] & Weakly supervised CNN & — & — & 72.86\% & — & — \\ \hline
**Proposed** &
\begin{tabular}{c} **Deeplabv3+ with** \\ **ResNet-50 backbone and** \\ **Dice loss function** \\ \end{tabular} & **97.18\%** & **90.49\%** & **85.60\%** & **94.69\%** & **89.81\%** \\ \hline
**Proposed** &
\begin{tabular}{c} **Deeplabv3+ with** \\ **ResNet-50 backbone and** \\ **Dice loss function** \\ \end{tabular} & **Firefront_Gestosa** & **99.97\%** & **92.61\%** & **79.09\%** & **99.95\%** & **98.60\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Resume of the state-of-art, mainly the algorithms tested over FLAME and Corsican datasets: comparison with the current work. however that would be more suited with a larger dataset. So, it will be considered in future work. Moreover, we will consider a bigger labeled set of Firefront_Gestosa pictures. Conceptualization, J.M.P.N. and A.B. ; methodology, H.H. ; software, H.H. ; validation, J.M.P.N. and A.B. ; formal analysis, H.H. ; investigation, H.H. ; resources, J.M.P.N. ; data curation, H.H. ; writing--original draft preparation, H.H. and H.F.T.A. ; writing--review and editing, H.H. and J.M.P.N. and H.F.T.A. ; visualization, H.H. and J.M.P.N. ; supervision, J.M.P.N. ; project administration, A.B. ; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.