# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Dataset Description  
The proposed dataset consists of 73 Areas of Interest (AoI) gathered from different European Regions by Copernicus EMS, which provides severity prediction and wildfire delineation. For each of them, the service provides the four geographical coordinates which define the AoI and the corresponding reference date. Such information has been used to retrieve and select the most suitable satellite acquisitions from SentinelHub service [72]. Accepted regions are selected if the following constraints are satisfied [50]: (i) the satellite acquisition must stay within the accepted range of the reference date, (ii) the data acquisition must be available for at least the 90% of the desired AoI, and (iii) cloud coverage must not exceed 10% of the AoI. Specifically, we define the accepted range of dates as the time interval ranging from one month prior to one month following the activation date, a date made available by Copernicus EMS. Thus, the accepted time interval is 2 months. Furthermore, an extra analysis of coherence between the acquisition and the delineation map has been performed by calculating the delta Normalized Burnt Ratio (dNBR) between the pre-fire and post-fire images. At this point, note that it is uncommon that some labeled wildfires take into account information not totally contained in satellite images. For instance, this can happen when the delineation map refers to recent events but neglects older ones that are still visible in the AoI. Moreover, also concerning severity estimation, human annotators join information obtained both from satellite images and from on-site patrols. Ignoring this consideration may lead to treating both the effective networks' mistakes and situations in which predictions differ from the ground truth as errors of the same importance but they are still coherent with the satellite inputs. To reduce this problem, only AoIs having a sensible correlation between dNBR (calculated through the guidelines from [31] and filtering water as described in [73]) and grading map are considered in the construction of the dataset. In the so-constructed dataset, each AoI, identified by a conventional product name, is associated with pre- and post-fire images collected from Sentinel-1, Sentinel-2, and the corresponding Digital Elevation Model (DEM). In this paper, only Sentinel-2 (L2A products) post-fire acquisitions are considered for the analysis, which consists of 12 channels for each image. Therefore, network inputs represent terrain areas with matrices of size \(W\times H\times D\), where \(W\) and \(H\) are respectively width and height (with dimensions up to \(5000\times 5000\)), and \(D=12\) represents the 12 channels acquired by satellite sensors. Finally, each sample presents the pixel-wise ground-truth grading map with 5 severity levels, corresponding to the damage intensity caused by the wildfire, ranging from 0 (no damage) to 4 (completely destroyed). As the image resolutions are too high to be directly used to train the neural networks, they are split into square tiles of size \(480\times 480\) and passed to the neural network in batches of size 16. Moreover, only tiles containing at least one burnt pixel are taken into account, ending up with a total number of 475 tiles. Assuming that acquisitions belonging to neighboring regions typically share the same morphology, data are split into folds based on the geographical proximity of the AoIs. This choice has been made to reduce the leakage effect in the training phase. In this way, we formed 7 different folds that we indicated with color names. We adopt this separation for further cross-validation. As shown in Figure 4, the so-defined folds present different distributions of pixels from the 5 severity levels, which in any case are not equally distributed even in the complete dataset. Moreover, different folds present significantly different distributions of the severity levels, which confirms the difficulty of the prediction task.