# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 5 Conclusions and Future Works  
This paper presents an extensive experimental evaluation of a double-step CNN solution in predicting areas damaged by forest wildfires and their damage assessment without the need of pre-fire images. Experimental results on a very large real-world satellite dataset demonstrate the power of the Double-Step deep learning Framework: most of the proposed network-loss combinations obtain high-quality results across the whole dataset.  
The most performant DSF achieved an rMSE score of 1.30 on burnt classes, leading in the majority of cases to mean errors of one severity (neighboring) class, thus providing useful information for preliminary analyses of areas affected by wildfires through remote sensing images. We further emphasize the fact that the aforementioned rMSE value is computed considering only burnt regions.  
The Attention-based network we introduced has been shown to maintain high performance in the prediction in association with the possibility to inspect the deep learning model's behavior. Domain knowledge has been applied to aggregate the satellite input channels and investigate their effects on the network learning process. The multi-channel attention-based analysis allowed us to identify groups of channels with different importance with respect to the prediction task. To this aim, we also introduced an attention-based occlusion method to increase the network interpretability. This strategy allows a whole-dataset measure of the network attention strength. An extension to this procedure is the implementation of such layers to an image- or pixel-wise magnitude. This means the possibility to provide to domain experts both a prediction of the damaged region and a clearer explanation of the process the network made to give such a result.  
Future directions of this work include a deeper use of the attention layer to enhance the final prediction and offer a clearer way to solve common errors in the detection. Additionally, the benefits of a multi-step compound model can be further analyzed by including domain knowledge on spectral bands and eventually build ensemble models to better exploit all the available information.  
Conceptualization, S.M., S.G., A.F. and D.A.; methodology, A.F., D.A., P.G., E.B. and T.C.; software, S.M., S.G. and L.C.; investigation, S.M., S.G., A.F., L.C. and D.A.; writing--original draft preparation, S.M., S.G. and L.C.; writing--review and editing, A.F., D.A., P.G., E.B. and  
Figure 9: Visualization of the all_occlusion results for the different Multi-bands. Yellow contours indicate the severity scale region of the normal prediction. The perturbed prediction is indicated via the colored regions as in the legend.  
T.C.; visualization, S.M. and S.G.; supervision, A.F., D.A., P.G. E.B. and T.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the RESCUE project and the SmartData@PoliTO research center.  
Not applicable.  
The data presented in this study are available upon request to the corresponding author and are also available through a GitHub public repository linked in the paper, together with the full source code.  
The research leading to these results has been partially supported by the SmartData@PoliTO center for Big Data and Machine Learning technologies, and the HPC@PoliTO center for High Performance Computing. The authors are grateful to [PERSON] for his help in exploiting the HPC resources.  
The authors declare no conflict of interest.