# A review of machine learning applications in wildfire science and management

arXiv: https://arxiv.org/abs/2003.00646 · HTML: https://mlsanity.com/paper/2003.00646/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 30 of 65)

- [Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data](https://mlsanity.com/paper/2112.02447/) (2021)
- [A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management](https://mlsanity.com/paper/2401.02456/) (2024)
- [Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark](https://mlsanity.com/paper/2112.00570/) (2021)
- [Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean](https://mlsanity.com/paper/2306.05144/) (2023)
- [Deep Learning Methods for Daily Wildfire Danger Forecasting](https://mlsanity.com/paper/2111.02736/) (2021)
- [From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs](https://mlsanity.com/paper/2502.17701/) (2025)
- [Deep Learning for Global Wildfire Forecasting](https://mlsanity.com/paper/2211.00534/) (2022)
- [TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting](https://mlsanity.com/paper/2306.10940/) (2023)
- [A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model](https://mlsanity.com/paper/2507.00761/) (2025)
- [A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models](https://mlsanity.com/paper/2206.08523/) (2022)
- [A generative model for surrogates of spatial-temporal wildfire nowcasting](https://mlsanity.com/paper/2308.02810/) (2023)
- [Location-aware Adaptive Normalization: A Deep Learning Approach For Wildfire Danger Forecasting](https://mlsanity.com/paper/2212.08208/) (2022)
- [FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction](https://mlsanity.com/paper/2502.01550/) (2025)
- [Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/2401.11582/) (2024)
- [Multi-Agent Advisor Q-Learning](https://mlsanity.com/paper/2111.00345/) (2021)
- [Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior](https://mlsanity.com/paper/2210.16411/) (2022)
- [FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2](https://mlsanity.com/paper/2311.03339/) (2023)
- [Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data](https://mlsanity.com/paper/2503.14150/) (2025)
- [Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting](https://mlsanity.com/paper/2509.25017/) (2025)
- [Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution](https://mlsanity.com/paper/2204.11350/) (2022)
- [AutoML for Climate Change: A Call to Action](https://mlsanity.com/paper/2210.03324/) (2022)
- [Explainable AI Integrated Feature Engineering for Wildfire Prediction](https://mlsanity.com/paper/2404.01487/) (2024)
- [Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction](https://mlsanity.com/paper/2411.09844/) (2024)
- [Conformal Risk Control for Safety-Critical Wildfire Evacuation Mapping: A Comparative Study of Tabular, Spatial, and Graph-Based Models](https://mlsanity.com/paper/2603.22331/) (2026)
- [Multi-time Predictions of Wildfire Grid Map using Remote Sensing Local Data](https://mlsanity.com/paper/2209.10102/) (2022)
- [Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management](https://mlsanity.com/paper/2310.01593/) (2023)
- [Preliminary Wildfire Detection Using State-of-the-art PTZ (Pan, Tilt, Zoom) Camera Technology and Convolutional Neural Networks](https://mlsanity.com/paper/2109.05083/) (2021)
- [Learning Wildfire Model from Incomplete State Observations](https://mlsanity.com/paper/2111.14038/) (2021)
- [A Multi-Modal Wildfire Prediction and Personalized Early-Warning System Based on a Novel Machine Learning Framework](https://mlsanity.com/paper/2208.09079/) (2022)
- [A Neural Emulator for Uncertainty Estimation of Fire Propagation](https://mlsanity.com/paper/2305.06139/) (2023)

## Abstract (arXiv)

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.