# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 2 Related Work
### Explainable Artificial Intelligence  
Despite the high accuracy of deep neural networks such as CNN on image classification and segmentation tasks, their complex architecture hides their decision-making process. The lack of comprehensibility increases the need to explain and understand their decisions. This has led to the growth of a new research area called eXplainable Artificial Intelligence (XAI).  
The different literature explainability techniques can be classified according to various criteria [55]. The first possible classification is by the scope of the explanation: (i) local explanation and (ii) global explanation. The former aims to explain single predictions of the model, while the latter explains the model at a global level. Then, the explanations can be classified as intrinsic if they are obtained by approximating them with an interpretable white-box model, and in post hoc, if they analyze the model after training. A further classification is (i) model-agnostic and (ii) model-specific. Model-agnostic methods can be applied to any model, while model-specific techniques are limited to specific model architectures. Finally, explanation methodologies can be classified by how they are produced, and the most common are: (i) gradient-based and (ii) perturbation-based explanations.  
Gradient-based explanations [56; 57] are powerful methods that measure the spatial importance of a particular class in images by exploiting the gradients of the outputs over inputs and class activation mapping (CAM) [58] to show relevant portions of the input images. Other similar techniques instead exploit saliency maps [59; 60; 61]. In contrast, perturbation-based explanations evaluate how the outputs are affected by inputs' changes. For instance, the work in [62] uses a model-agnostic perturbation-based technique that produces local explanations by training a simpler interpretable model that approximates the prediction locally. Instead, the work in [63] produces local explanations of deep convolutional neural networks by a process of perturbation over interpretable features extracted from the hypercolumns. The authors of [64] propose a model-agnostic explainability technique based on a game-theoretic approach that iteratively removes combinations of inputs features to measure the features' importance.  
Both gradient- and perturbation-based methods usually exploit heatmaps or similar techniques to visualize the relevant pixels on the RGB channels to produce explanations. However, most of these techniques were originally intended to explain classification models. A first attempt to perform explainability on segmentation models was proposed in [65]. It implements various gradient-based visualization methods helping to understand and visualize different layers of neural networks to explain its predictions. In our case, landing data for wildfire severity prediction is composed of complex multi-channel inputs with different bands besides RGB, making human understandability difficult. Moreover, the aggregations of different channels have different and important domain-driven meanings.  
This increases the need to analyze inputs based on the domain semantics. Therefore, an explanation that visualizes only RGB channels would not be effective, especially for domain experts.  
Differently from previous works, in this paper, we combine a model-aware attention-driven post hoc perturbation technique with landing wildfire domain knowledge to globally explain deep convolutional segmentation models by understanding the most relevant inputs macro-bands. To the best of our knowledge, this is the first attempt to interpreting and explaining the segmentation of deep neural networks for wildfire severity prediction.