Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction

Jonas V. Funk

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arXiv · 2605.03148

Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction

Jonas V. Funk

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Abstract

Reliable wildfire spread prediction is vital for risk-aware emergency planning, yet most deep learning models lack principled uncertainty quantification (UQ). Further, for boundary-sensitive cases like wildfire spread, evaluating models with global metrics alone is often insufficient. To shift the focus of UQ evaluation toward a more operationally relevant approach, the Fire-Centered Evaluation Region (FCER) framework is introduced as a spatially conditioned protocol to characterize UQ within critical fire zones. Using FCER, an Ensemble is compared against an distilled single-pass student model on the WildfireSpreadTS dataset. The student model demonstrates comparable calibration and complementary uncertainty ranking in boundary-relevant regimes. Code is available at https://github.com/jonasvilhofunk/WildfireUQ-FCER

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Jonas V. Funk. 2026-05-04. Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction. https://arxiv.org/abs/2605.03148

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