构建了高精度西班牙火灾风险数据集IberFire，支持精准建模与防灾决策。
IberFire -- a detailed creation of a spatio-temporal dataset for wildfire risk assessment in Spain
- 整合120项多源特征，以1km×1km×1天分辨率覆盖2007–2024年西班牙本土与巴利阿里群岛
- 相比现有欧洲数据集，空间粒度更细、特征维度更高，支持机器学习建模
- 全开源数据与代码，适合气候研究、灾害预警与土地管理相关领域使用
野火对生态系统、经济和公共安全构成威胁，尤其在西班牙等地中海地区。精准预测模型依赖高分辨率时空数据以捕捉环境与人为因素的复杂动态。为解决西班牙细粒度野火数据稀缺问题，我们提出IberFire：一个空间分辨率为1 km × 1 km、时间分辨率为1天，覆盖2007年12月至2024年12月西班牙本土及巴利阿里群岛的时空数据集。IberFire整合了8类共120个特征，包括辅助数据、火灾历史、地理、地形、气象、植被指数、人类活动和土地利用。所有数据与处理流程基于公开可获取的数据与工具，配套开源代码库保障透明性与可复现性。相比现有欧洲数据集，IberFire具备更高的空间粒度与特征多样性，支持机器学习与深度学习驱动的火灾风险建模，助力气候变化趋势分析，并为防火策略与土地管理提供科学依据。该数据集已免费发布于Zenodo，促进开放研究与协作。
原文摘要 · Abstract (English)
Wildfires pose a threat to ecosystems, economies and public safety, particularly in Mediterranean regions such as Spain. Accurate predictive models require high-resolution spatio-temporal data to capture complex dynamics of environmental and human factors. To address the scarcity of fine-grained wildfire datasets in Spain, we introduce IberFire: a spatio-temporal dataset with 1 km x 1 km x 1-day resolution, covering mainland Spain and the Balearic Islands from December 2007 to December 2024. IberFire integrates 120 features across eight categories: auxiliary data, fire history, geography, topography, meteorology, vegetation indices, human activity and land cover. All features and processing rely on open-access data and tools, with a publicly available codebase ensuring transparency and applicability. IberFire offers enhanced spatial granularity and feature diversity compared to existing European datasets, and provides a reproducible framework. It supports advanced wildfire risk modelling via Machine Learning and Deep Learning, facilitates climate trend analysis, and informs fire prevention and land management strategies. The dataset is freely available on Zenodo to promote open research and collaboration.
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