Spain on Fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information

• URL: http://arxiv.org/abs/2306.05045v1
• Date: Thu, 8 Jun 2023 08:55:16 GMT
• Title: Spain on Fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information
• Authors: Helena Liz-L\'opez, Javier Huertas-Tato, Jorge P\'erez-Aracil, Carlos Casanova-Mateo, Julia Sanz-Justo, David Camacho
• Abstract summary: Wildfires destroy larger areas of Spain each year, threatening numerous ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour of individuals is unpredictable. In order to mitigate the damage of these events we proposed the novel Wildfire Assessment Model (WAM). Our aim is to anticipate the economic and ecological impact of a wildfire, assisting managers resource allocation and decision making for dangerous regions in Spain.
• Score: 1.8377229717030112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Each year, wildfires destroy larger areas of Spain, threatening numerous ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour of individuals is unpredictable. However, atmospheric and environmental variables affect the spread of wildfires, and they can be analysed by using deep learning. In order to mitigate the damage of these events we proposed the novel Wildfire Assessment Model (WAM). Our aim is to anticipate the economic and ecological impact of a wildfire, assisting managers resource allocation and decision making for dangerous regions in Spain, Castilla y Le\'on and Andaluc\'ia. The WAM uses a residual-style convolutional network architecture to perform regression over atmospheric variables and the greenness index, computing necessary resources, the control and extinction time, and the expected burnt surface area. It is first pre-trained with self-supervision over 100,000 examples of unlabelled data with a masked patch prediction objective and fine-tuned using 311 samples of wildfires. The pretraining allows the model to understand situations, outclassing baselines with a 1,4%, 3,7% and 9% improvement estimating human, heavy and aerial resources; 21% and 10,2% in expected extinction and control time; and 18,8% in expected burnt area. Using the WAM we provide an example assessment map of Castilla y Le\'on, visualizing the expected resources over an entire region.

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