FireAID - Real-time Wildfire Spread Modeling with Machine Learning

Wildfires are a major type of disaster and challenge for economic prosperity, public health and safety around the globe. Decision Intelligence, particularly AI based scenario analysis, can make a significant difference [1] in disaster mitigation efforts. Data-driven methods have shown promise in various downstream applications [2]. Still, reference data remains a significant bottleneck across domains such as fire behaviour modeling.

We develop three data-driven decision intelligence tools: a novel machine learning based fire spread model, a fire break placement recommender, and triage decision support.
We make use of data from OroraTech’s global near-real-time fire monitoring network, which provides hotspot data from both public and proprietary satellites, in addition to burned area products.
We have created a novel dataset with thousands of fires from the US, Chile and Europe between 2022-2025. We enriched the thermal hotspot-based fire perimeters with a variety of EO (land cover, soil moisture, elevation, previously burned area, vegetation index) and non-EO (wind, temperature, relative humidity, dew point, and precipitation) data.

With this dataset, we train fire spread prediction models based on leading DL architectures. Graph Neural Networks (GNN) are particularly promising, since they have excelled in related domains such as weather forecasting [3], and showed promising spatial generalization properties for fire spread [4]. To mitigate uneven satellite overpass intervals, we treat the time gap between input-target images as an additional learning signal.
A major hurdle in the operational use of fire intelligence tools is a lack of user trust. Therefore, we incorporate explainability metrics in all three of key contributions.

The use of fire breaks - creating “barriers” of non-burnable materials to prevent fires from spreading - is a significant tactic in wildfire management. Scenario analysis tools are essential to inform the placement of fire breaks. Despite recent progress, significant challenges remain in this domain, such as reliance on basic fire spread simulators, and a complex action space for fire break placement [1]. We aim to close this gap by coupling our improved fire spread model combined with reinforcement learning, a promising approach pioneered in a recent case study [1] for fire break recommendations.

In conclusion, we present a novel fire dataset and operational tools for global, real-time fire spread modeling and  firebreak placement supporting wildfire management worldwide. 

References

[1]Murray,L.,Castillo,T.,Carrasco,J.,Weintraub,A.,Weber,R.,deDiego,I.M.,...&GarcíaGonzalo,J.(2024).Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement.arXiv preprint arXiv:2404.08523.

[2]Bot,K.,&Borges,J.G.(2022).A systematic review of applications of machine learning techniques for wildfire management decision support.Inventions,7(1),15.

[3]Lam,R.,Sanchez-Gonzalez,A.,Willson,M.,Wirnsberger,P.,Fortunato,M.,Alet,F.,...&Battaglia,P.(2023).Learning skillful medium-range global weather forecasting.Science,382(6677),1416-1421.

[4]Rösch,M.,Nolde,M.,Ullmann,T.,&Riedlinger,T.(2024).Data-Driven Wildfire Spread Modeling of European Wildfires Using a Spatiotemporal Graph Neural Network.Fire,7(6),207.