用深度学习预测野火蔓延范围，多日数据提升预测准确率。
Wildfire spread forecasting with Deep Learning
- 基于燃点前后多日数据构建时空预测模型
- 模型在测试集上F1和交并比提升近5%
- 适合灾害预警与应急资源规划人员使用
准确预测野火蔓延范围对风险管控、应急响应和资源调配至关重要。本文提出一种基于深度学习（DL）的框架，利用燃点时刻可获取的数据预测最终过火面积。研究采用2006至2022年覆盖地中海地区的时空数据集，包含遥感数据、气象观测、植被图、土地利用分类、人为因素、地形数据及热异常信息。通过消融实验评估时间上下文的影响，对比仅使用燃点日数据的基线模型与包含燃点前4天至后5天数据的时序感知模型。结果表明，多日观测数据显著提升预测精度：最佳模型在测试集上较基线提升近5%的F1分数和交并比。研究公开发布数据集与模型，以推动数据驱动型野火建模与响应研究。
原文摘要 · Abstract (English)
Accurate prediction of wildfire spread is crucial for effective risk management, emergency response, and strategic resource allocation. In this study, we present a deep learning (DL)-based framework for forecasting the final extent of burned areas, using data available at the time of ignition. We leverage a spatio-temporal dataset that covers the Mediterranean region from 2006 to 2022, incorporating remote sensing data, meteorological observations, vegetation maps, land cover classifications, anthropogenic factors, topography data, and thermal anomalies. To evaluate the influence of temporal context, we conduct an ablation study examining how the inclusion of pre- and post-ignition data affects model performance, benchmarking the temporal-aware DL models against a baseline trained exclusively on ignition-day inputs. Our results indicate that multi-day observational data substantially improve predictive accuracy. Particularly, the best-performing model, incorporating a temporal window of four days before to five days after ignition, improves both the F1 score and the Intersection over Union by almost 5% in comparison to the baseline on the test dataset. We publicly release our dataset and models to enhance research into data-driven approaches for wildfire modeling and response.
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