Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction
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Computer Science > Machine Learning
Title:Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction
Abstract: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.
| Comments: | Accepted at 22nd AIAI 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.07472 [cs.LG] |
| (or arXiv:2608.07472v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.07472
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