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PyroAdapt proposes a pretrain-retrieve-rank framework to adapt wildfire prediction models for spatial heterogeneity and temporal distribution shifts, improving detection accuracy and prioritizing fire-prone locations under budget constraints.
This paper introduces WildfireSpreadBench to benchmark wildfire spread prediction models, revealing that evaluation metrics like AP versus F1 can lead to different model rankings and affect operational suitability.
This study proposes modular deep learning augmentations for next-day wildfire spread prediction, enhancing audibility and trustworthiness through attention biases, retrieval augmentation, and dual-stream gating, evaluated on benchmarks with performance metrics.
This paper proposes an unsupervised fire-zone segmentation method combining watershed detection with K-means clustering to improve short-term wildfire prediction, showing consistent gains over grid-based approaches across multiple French departments and forecasting models.
This paper introduces a physics-guided machine learning framework that integrates physical constraints into deep learning models (ConvLSTM, AFNONet, ViViT) to predict fuel density for wildfire management, outperforming purely data-driven approaches.