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

arXiv: https://arxiv.org/abs/2306.05045 · HTML: https://mlsanity.com/paper/2306.05045/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 72 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Masked Autoencoders for Point Cloud Self-supervised Learning](https://mlsanity.com/paper/2203.06604/) (2022)
- [Masked Autoencoders As Spatiotemporal Learners](https://mlsanity.com/paper/2205.09113/) (2022)
- [A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder](https://mlsanity.com/paper/2009.11990/) (2020)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis](https://mlsanity.com/paper/2010.02007/) (2020)
- [Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review](https://mlsanity.com/paper/2207.07580/) (2022)
- [Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques](https://mlsanity.com/paper/2209.15424/) (2022)

## Built on by (most cited first, 1 of 1)

- [IberFire -- a detailed creation of a spatio-temporal dataset for wildfire risk assessment in Spain](https://mlsanity.com/paper/2505.00837/) (2025)

## Abstract (arXiv)

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.