# Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction

arXiv: https://arxiv.org/abs/2608.07472 · HTML: https://mlsanity.com/paper/2608.07472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data](https://mlsanity.com/paper/2112.02447/) (2021)
- [Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean](https://mlsanity.com/paper/2306.05144/) (2023)
- [FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction](https://mlsanity.com/paper/2502.01550/) (2025)
- [Localized Forest Fire Risk Prediction: A Department-Aware Approach for Operational Decision Support](https://mlsanity.com/paper/2506.04254/) (2025)

## Abstract (arXiv)

Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions. We challenge this paradigm by showing that how data is discretized matters more than which model is used. We propose an unsupervised fire-zone segmentation algorithm combining watershed detection with K-means clustering to define prediction units directly from historical fire patterns. Experiments across six French departments and six forecasting models show that fire-zone segmentation consistently outperforms grid-based approaches, with mean IoU improvements of +3--6% depending on spatial scale. The method is computationally lightweight (<10s per configuration) and fully parallelizable. Our results demonstrate that optimizing spatial discretization yields significant, reproducible performance gains for short-term wildfire forecasting.