# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 2 Related Work  
In this section, we first review previous works on different strategies for wildfire prediction. Then, moving to the CNN solutions, we analyze the state-of-the-art architectures addressing semantic segmentation problems, and we focus on applying these techniques to our task, highlighting the differences with our proposed solution. Finally, in Section 2.1, we provide an overview of the available explainability techniques suitable for the computer vision domain. Burnt area delineation is a well-known problem in remote sensing literature. Many approaches address this task by exploiting different satellite indexes techniques [14; 15], eventually assessing a severity level estimation [16; 17]. Satellites collect information across multiple bandwidths which are sensitive to different land features. Therefore, combinations of specific acquisition channels can be used to detect the presence of water bodies [18], vegetation [19], burnt areas [20], snow, and, in principle, any kind of terrain [21; 22; 23; 24] with the application of thresholding strategies [25] such as the [PERSON]'s method [26; 27]. These combinations of acquisition channels are known in the literature as indexes. Normalized Burn Ratio (NBR) [28; 29] and Burned Area Index for Sentinel-2 (BAIS2) [20] are some of the aforementioned indexes specifically designed for burned area detection. Considering the 12 bandwidths available from Sentinel-2 L2A products, NBR is the normalized difference between the Near-Infrared (NIR) and the short wavelength infrared (SWIR) channels (8 and 12, respectively). BAIS2 instead combines vegetation properties obtained from a band ratio in the red-edge spectral domain (bands 6 and 7) and a band ratio involving bands 8A and 12, recognized to be efficient in determining burnt regions. Further indexes can be obtained by comparing pre-fire and post-fire acquisitions, as for delta Normalized Burn Ratio (dNBR) [16], computed as \(\text{dNBR}=\text{NBR}_{PRE}-\text{NBR}_{POST}\). Then, the dNBR can be quantized to generate a severity scale. This approach is computationally fast and is generally considered very accurate. Therefore, it is still largely adopted by the scientific community [17; 30]. The European Forest Fire Information Service (EFFIS) of Copernicus program itself provides, in fact, an adjusted threshold for dNBR to identify four different severity levels [31]. Severity prediction based on manual or automatic thresholding of these indexes is addressed with a wide range of techniques [25; 32; 33]. However, dNBR also requires acquisitions taken before the wildfire event, which might be difficult to obtain. Depending on season and weather conditions, atmosphere, foliage, and ground morphology can sensibly change. Moreover, cloud coverage can affect the visibility of the areas of interest (AoI), making the comparison between pre- and post-fire images difficult overall. Summing up, generated maps such as the ones provided by Copernicus EMS requires several days to be completed and an eventual manual intervention, as the homogeneity between the two events is not guaranteed. Some mapping operations are performed based on in situ information, such as the Composite Burned Index (CBI) [34], but these are time-consuming and require a large amount of extra data such as the evaluations of the soil and vegetation conditions for the entire AoI. These limitations offer the opportunity to investigate many different approaches, eventually involving machine learning and deep learning-based techniques, such as in [35; 36]. Deep learning strategies have been largely adopted in the past to address many kinds of predictions concerning wildfire events. Some techniques, such as those in [37; 38], can, for instance, be used to monitor the evolution of wildfires during the event to support domain experts. Moving to the damaged region analysis, our proposed solutions solve the aforementioned issues by only considering post-fire images and applying a supervised prediction approach on pre-labeled severity maps.