## 1 Introduction
[4] and [PERSON] [32] addressed visible-infrared image fusion with traditional methods. In recent years, there has been a growing interest in DL as a technique for image fusion. This is due to its reduced complexity compared to methods on the multi-scale transform and representation learning domains [33]. Up to now, the only DL-based approach model was developed by [PERSON] et al. [34]. In the context of wildfire detection, several approaches have been proposed for both fire image classification (as mentioned previously, to classify a full image as either fire or non-fire) [8; 9; 10; 35] and fire semantic segmentation. [5; 36; 37; 38; 39]. DL-based wildfire semantic segmentation methods seek to classify each pixel in an image as fire or non-fire. Figure 1 shows an example of wildfire image classification and segmentation. Most of the existing DL-based wildfire segmentation methods employ visible images; for the particular context of DL-based wildfire segmentation, it is still unclear if the inclusion of fused information would enable a significant improvement in the fire segmentation performance of a model or if factors such as the architecture and loss function play a more relevant role in the said performance. In order to investigate these questions, in this work, we train three SOTA DL architectures [5; 36; 38], coupled with three loss functions (Dice [40], Focal Tversky [41], and Unified Focal [42]) and four fire image types (visible, near-infrared (NIR), and fused generated from two methods [29; 34]). Then, we evaluate the resulting thirty-six combinations to assess the impact of each of the mentioned parameters in the wildfire segmentation performance. We use standard metrics to compare the segmented images to their corresponding ground truths to identify the best performing combination. We employ the Matthews Correlation Coefficient (MCC) [43], the F1 score [44], and the Hafiane quality index (HAF) [45] as in the work by [PERSON] et al. [11] to benchmark the best identified combination against the traditional methods evaluated by [PERSON] et al. as baselines. Finally, we explore the use of attention modules [46; 47; 48] for this particular segmentation task. The main contributions of this work are two-fold:  
* We perform a comprehensive evaluation of thirty-six combinations of three selected architectures and loss functions, as well as four image types, to assess which of these elements affects wildfire segmentation performance the most, exploring as well the use of attention modules for the particular task of fire segmentation. * We benchmark the best combination against traditional fire segmentation methods to assess if it provides a significant advantage over them. The rest of this paper proceeds as follows. First, Section 2 introduces related work in the area of DL-based semantic segmentation. Afterward, in Section 3, we present the datasets, architectures, loss functions, image types and metrics employed in this study. In Section 4, we discuss the results of the generated combinations, as well as the benchmarking results against traditional methods. Finally, in Section 5, we embark on a discussion of the obtained results, the conclusions, and potential avenues for future work.