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Each year, wildfires destroy larger areas of Spain, threatening numerousecosystems. Humans cause 90% of them (negligence or provoked) and the behaviourof individuals is unpredictable. However, atmospheric and environmentalvariables affect the spread of wildfires, and they can be analysed by usingdeep learning. In order to mitigate the damage of these events we proposed thenovel Wildfire Assessment Model (WAM). Our aim is to anticipate the economicand ecological impact of a wildfire, assisting managers resource allocation anddecision making for dangerous regions in Spain, Castilla y Le\'on andAndaluc\'ia. The WAM uses a residual-style convolutional network architectureto perform regression over atmospheric variables and the greenness index,computing necessary resources, the control and extinction time, and theexpected burnt surface area. It is first pre-trained with self-supervision over100,000 examples of unlabelled data with a masked patch prediction objectiveand fine-tuned using 311 samples of wildfires. The pretraining allows the modelto understand situations, outclassing baselines with a 1,4%, 3,7% and 9%improvement estimating human, heavy and aerial resources; 21% and 10,2% inexpected extinction and control time; and 18,8% in expected burnt area. Usingthe WAM we provide an example assessment map of Castilla y Le\'on, visualizingthe expected resources over an entire region.

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