## 2 Related Work
### Deep Learning-Based Wildfire Segmentation  
As discussed in Section 1, we are interested in the task of wildfire segmentation as it allows us to obtain information like the position, rate of spread, height, inclination, surface, and volume of the fire, which are relevant characteristics that play a significant role in the development and improvement of fire behavior models [11]. In this section, we describe some of the most relevant works pertaining to DL-based wildfire segmentation. [PERSON] et al. [38] propose the Deep-Fire network, a deep convolutional neural network (DCNN) based on the U-Net architecture. They employed visible \(RGB\) images of forest fires as inputs. The model outputs a binary mask representing the fire pixels in an image. The authors then use the Corsican Fire Database [71] for the training and testing of their model. [PERSON] et al. report good results with the Dice similarity coefficient as the loss function for the model, with an F1-Score ranging between 64.2% and 99% on the test set. [PERSON] et al. [37] train the Deeplabv3+ architecture on wildfire images from the Corsican Fire Database. The authors employ the Dice similarity and Tversky loss functions with cross-entropy. Additionally, they test the model with two different backbones, ResNet-18 and ResNet-50. Finally, [PERSON] et al. identify the Dice loss and the ResNet-50 backbones as the best performing combination, reporting an accuracy of 97.53%. [PERSON] et al. [36] propose a DCNN model that generates fire and smoke segmentation masks taking visible \(RGB\) images as inputs. The authors propose an architecture based on the VGG16 network [58] for the coding phase. [PERSON] et al. use transpose convolutions for up-sampling in the decoding phase and add skip connections to several layers of the coding phase. They collected visible images containing fire and smoke from the internet and manually segmented them to construct the dataset they used for training and testing the model. Finally, the authors report an average accuracy of 98% for this model. [PERSON] et al. [5] propose a DCNN similar to FusionNet for fire segmentation. They implement an encoder-decoder architecture, with skip connections between encoding and decoding layers in a U-Net-like fashion. The authors implemented ResNet blocks instead of traditional convolutional blocks for both the encoding and decoding process alongside simple convolution and deconvolution layers. [PERSON] et al. train and test the model in the \begin{table}
\begin{tabular}{l l l} \hline \hline
**Approach** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{VGG-based} & Simple, straightforward & Large number of parameters; high \\  & architectures. & computational power required. \\  & Address the vanishing gradient & Large-scale usage of skip \\  & problem: allows for deeper & connections can lead to memory \\  & networks. & problems. \\ \multirow{2}{*}{DeepLab family} & Atrous convolutions to generate & ASPP-based methods display a \\  & high-resolution feature maps. & significant computational cost. \\  & Designed for good performance & Skip connections tend to use \\ U-Net-based models & on smaller datasets. Relatively & redundant information in low-level \\  & low number of parameters. & encoder features. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the advantages and disadvantages of the discussed approaches. FiSmo Dataset [72] and the Corsican Fire Database, using the mean square error (MSE) as the loss function. Finally, the model displays an accuracy of 99% on the FiSmo Dataset and a 97% accuracy on the Corsican Fire Database. Finally, the work proposed by [PERSON] et al. [39] is, to the best of our knowledge, the only DL model that leverages multispectral images to perform wildfire segmentation. The authors propose a DCNN that incorporates both spectral and spatial information that they obtain through the GOES-16 satellite. As spectral images have an additional dimension of spectral bands with partial dependencies between them [39], [PERSON] et al.