  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.
