## 6 Conclusions  
We identify the architecture and loss function as the elements with the most influence on the fire segmentation performance of a DL model. The inclusion of fused information does not appear to make a significant difference in the segmentation performance. Two of the image types (the visible and _Fire-GAN_ fused) that we used possess color information, and the other two are grayscale images (the NIR and _VGG19_ fused). Thus, the inclusion of color information appears to be of little value for the performance of a DL-based fire segmentation model, in contrast with traditional methods in which the color information is amongst the most relevant features. Additionally, the present paper analyses infrared and fused images that contain information on the NIR wavelength spectrum; however, the DL models themselves for both fusion and segmentation do not distinguish between particular types of infrared images (NIR, long-wave infrared (LWIR), amongst others). Thus, we expect them to extend seamlessly to other types of infrared images as long as an appropriate and consistent dataset is provided. It is relevant to note that the results of this study are representative of the images of the Corsican Fire Database only. This dataset contains images that are not challenging, that is, with no significant occlusion of the fire shape due to smoke, and with the fire occupying a relatively large region of the images. The latter means that there is little difference in the fire region present in the visible, NIR, and fused images, which could account for the lack of impact of the image type in the segmentation performance. It is highly likely that the image type, in particular the NIR and fused ones, would provide a more significant advantage in more challenging scenarios, e.g., with images with considerable smoke occlusion. Additionally, since the DL models for both fusion and segmentation were trained only in these non-challenging samples, their generalization capabilities to challenging, operative-scenario images may be limited. A promising avenue for further analysis is the inclusion and analysis of more loss functions relevant in the field of semantic segmentation, such as the Lovasz-Softmax loss [85], the Region Mutual Information (RMI) [86] loss, and the affinity loss [87]. Additionally, in recent times, there has been work proposing different training paradigms for DL-based semantic segmentation, such as the pixel-wise contrastive framework proposed by [PERSON] et al. [88]. The inclusion of the training paradigm as an additional parameter can provide further insight into the relevance of the different elements involved in DL-based wildfire segmentation performance. Finally, given the ability of the fused images to preserve both thermal and textural information, we can expect them to provide further advantages for smoke and fire segmentation. The generation of more challenging datasets containing visible-infrared image pairs with ground truths for fire and smoke segmentation arises as a promising path for future work. Additionally, exploring few-shot learning approaches that can enable DL-based image fusion and segmentation models to learn and generalize with the limited visible-infrared fire datasets currently available is another promising avenue for further research. Conceptualization, J.F.C.-S. and G.O.-R.; methodology, J.F.C.-S. and G.O.-R.; software, J.F.C.-S.; validation, J.F.C.-S.; formal analysis, J.F.C.-S.; investigation, J.F.C.-S., G.O.-R. and L.R. ; resources, G.O.-R., L.R. and F.M. ; data curation, L.R. and F.M. ; writing--original draft preparation, J.F.C.-S.; writing--review and editing, G.O.-R., L.R. and F.M. ; visualization, J.F.C.-S.; supervision, G.O.-R.; project administration, G.O.-R.; funding acquisition, J.F.C.-S., G.O.-R. and L.R. All authors have read and agreed to the published version of the manuscript.