Wildfires pose a critical environmental issue to ecosystems, economies, and public safety, particularly in Mediterranean regions such as Spain. Accurate predictive models rely on high-resolution spatio-temporal data to capture the complex interplay of environmental and anthropogenic factors. To address the lack of localised and fine-grained datasets in Spain, this work introduces IberFire, a spatio-temporal datacube at 1 km x 1 km x 1-day resolution covering mainland Spain and the Balearic Islands from December 2007 to December 2024. IberFire integrates 260 features across eight main categories: auxiliary features, fire history, geography, topography, meteorology, vegetation indices, human activity, and land cover. All features are derived from open-access sources, ensuring transparency and real-time applicability. The data processing pipeline was implemented entirely using open-source tools, and the codebase has been made publicly available. This work not only enhances spatio-temporal granularity and feature diversity compared to existing European datacubes but also provides a reproducible methodology for constructing similar datasets. IberFire supports advanced wildfire risk modelling through Machine Learning (ML) and Deep Learning (DL) techniques, enables climate pattern analysis and informs strategic planning in fire prevention and land management. The dataset is publicly available on Zenodo to promote open research and collaboration.
View on arXiv@article{ercibengoa2025_2505.00837,
title={ IberFire -- a detailed creation of a spatio-temporal dataset for wildfire risk assessment in Spain },
author={ Julen Ercibengoa and Meritxell Gómez-Omella and Izaro Goienetxea },
journal={arXiv preprint arXiv:2505.00837},
year={ 2025 }
}