# FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction

arXiv: https://arxiv.org/abs/2502.01550 · HTML: https://mlsanity.com/paper/2502.01550/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [Forecasting Global Weather with Graph Neural Networks](https://mlsanity.com/paper/2202.07575/) (2022)
- [Delving Deeper into Convolutional Networks for Learning Video Representations](https://mlsanity.com/paper/1511.06432/) (2015)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series](https://mlsanity.com/paper/2007.00586/) (2020)
- [Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks](https://mlsanity.com/paper/2406.04759/) (2024)
- [Graph-based Neural Weather Prediction for Limited Area Modeling](https://mlsanity.com/paper/2309.17370/) (2023)
- [Deep Learning Methods for Daily Wildfire Danger Forecasting](https://mlsanity.com/paper/2111.02736/) (2021)
- [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)
- [Causal Graph Neural Networks for Wildfire Danger Prediction](https://mlsanity.com/paper/2403.08414/) (2024)
- [SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics](https://mlsanity.com/paper/2312.07199/) (2023)

## Built on by (most cited first, 7 of 7)

- [Localized Forest Fire Risk Prediction: A Department-Aware Approach for Operational Decision Support](https://mlsanity.com/paper/2506.04254/) (2025)
- [TeleViT1.0: Teleconnection-aware Vision Transformers for Subseasonal to Seasonal Wildfire Pattern Forecasts](https://mlsanity.com/paper/2512.00089/) (2025)
- [On the use of Graphs for Satellite Image Time Series](https://mlsanity.com/paper/2505.16685/) (2025)
- [CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities](https://mlsanity.com/paper/2506.08690/) (2025)
- [Predicting the Containment Time of California Wildfires Using Machine Learning](https://mlsanity.com/paper/2512.09835/) (2025)
- [Risk Is Not the Target: A Monotonic Framework for Evaluating Wildfire Operational Risk Signals](https://mlsanity.com/paper/2607.21597/) (2026)
- [Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction](https://mlsanity.com/paper/2608.07472/) (2026)

## Abstract (arXiv)

With climate change intensifying fire weather conditions globally, accurate seasonal wildfire forecasting has become critical for disaster preparedness and ecosystem management. We introduce FireCastNet, a novel deep learning architecture that combines 3D convolutional encoding with GraphCast-based Graph Neural Networks (GNNs) to model complex spatio-temporal dependencies for global wildfire prediction. Our approach leverages the SeasFire dataset, a comprehensive multivariate Earth system datacube containing climate, vegetation, and human-related variables, to forecast burned area patterns up to six months in advance. FireCastNet treats the Earth as an interconnected graph, enabling it to capture both local fire dynamics and long-range teleconnections that influence wildfire behavior across different spatial and temporal scales. Through comprehensive benchmarking against state-of-the-art models including GRU, Conv-GRU, Conv-LSTM, U-TAE, and TeleViT, we demonstrate that FireCastNet achieves superior performance in global burned area forecasting, with particularly strong results in fire-prone regions such as Africa, South America, and Southeast Asia. Our analysis reveals that longer input time-series significantly improve prediction robustness, while spatial context integration enhances model performance across extended forecasting horizons. Additionally, we implement local area modeling techniques that provide enhanced spatial resolution and accuracy for region-specific predictions. These findings highlight the importance of modeling Earth system interactions for long-term wildfire prediction.