# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 1 Introduction  
In the last decade, the attention to environmental problems significantly increased to safeguard natural resources [1, 2], preserve flora and fauna and the different ecosystems, and prevent catastrophic events and natural disasters. Among such natural disasters, forest fires demonstrated an increasing trend in recent years [3, 4], causing large losses and damages to ecosystems and municipalities. In this paper, we focus our attention on the increasing problem of forest fires, which are causes of both short- and long-term damages: economical loss, environmental damages [5, 6], and high risk for living beings, but also higher risk of soil erosion [7] and high recovery time due to reforestation. As a consequence of such disasters, the importance and the ability to monitor forests and affected areas are becoming crucial in decision-making processes led by authorities and governments [8]. As such, the need for proper tools and data is indeed relevant to the field: open data, aerial data acquisitions through aircraft, drones, and satellite imagery represent important ways to timely collect data and evaluate critical information, especially in times of natural disasters.  
Thanks to the availability of high-resolution images from Sentinel-1 and Sentinel-2 missions, the adoption of satellite acquisitions by the scientific community is growing. As an example, the work in [9] analyzed the problem of tree mortality leveraging on satellite imagery and forest surveys, whereas the work in [10] proposed the integration of multispectral satellite data for rapid assessment of forest damage caused by extreme events, such as storms. Therefore, the implementation of machine learning and deep learning models combined with large scale satellite imagery enables the research community andpublic authorities to analyze areas at a continental scale in a short amount of time, providing useful insights to decision-makers. More specifically, convolutional neural networks (CNNs) for satellite image analyses proved to be extremely effective in detecting forest wildfires and burnt area detection [11; 12; 13].  
In this paper, we focus on the adoption of state-of-the-art deep learning segmentation models applied to satellite acquisition to automatically detect areas damaged by previous forest wildfires and provide a discrete severity level estimation within fixed ranges. In particular, we investigate multi-channel explainability strategies driven by the introduction of an attention mechanism in the segmentation models. To the best of our knowledge, no previous works introduced an attention-driven mechanism to support the interpretability of multi-channel deep learning models in the field of automatic burned area detection, providing benefits in decision support systems and supporting local authorities' operations during the environmental monitoring processes.  
The novel contributions of the current work can be summarized as follows:  
* Extensive experimental comparison of a wide range of model architectures and loss functions for deep learning wildfire severity prediction models on real-world satellite data.
* Application of state-of-the-art deep-learning models to a very large dataset (24 GB) for wildfire severity prediction.
* Extension of a state-of-the-art Double-Step deep learning Framework (DSF) by introducing an attention mechanism to the UNet segmentation model.
* Analysis of multi-channel model interpretability by exploiting both the newly introduced attention-based mechanism and domain knowledge.  
The paper is structured as follows. Section 2 introduces the related works. Section 3 describes the proposed state-of-the-art Double-Step deep learning Framework (DSF) and all its components. Section 4 describes the dataset, the experimental design, and discusses the results. Finally, Section 5 presents conclusions and future developments.