FireSpread_MedEU
- Wildfires are becoming more frequent and severe under the influence of climate change, posing increasing risks to ecosystems, human health, and infrastructure. Accurate spatiotemporal data on wildfire propagation is essential for advancing fire behavior modeling, improving management strategies, and mitigating future impacts. However, existing datasets with both high spatial and temporal resolution are rare, costly, and time-consuming to produce. To address this gap, we present FireSpread_MedEU, a dataset comprising 313 consecutive burned area maps from 102 wildfire events across the Mediterranean and Europe between 2017 and 2023. Burned areas were derived from high-resolution Planet optical satellite imagery (~3 m spatial, mostly daily temporal resolution) using a semi-automated workflow, followed by manual refinement to ensure highest accuracy. Each dataset entry is enriched with detailed metadata and a subjective quality assessment. With its high level of spatiotemporal precision,Wildfires are becoming more frequent and severe under the influence of climate change, posing increasing risks to ecosystems, human health, and infrastructure. Accurate spatiotemporal data on wildfire propagation is essential for advancing fire behavior modeling, improving management strategies, and mitigating future impacts. However, existing datasets with both high spatial and temporal resolution are rare, costly, and time-consuming to produce. To address this gap, we present FireSpread_MedEU, a dataset comprising 313 consecutive burned area maps from 102 wildfire events across the Mediterranean and Europe between 2017 and 2023. Burned areas were derived from high-resolution Planet optical satellite imagery (~3 m spatial, mostly daily temporal resolution) using a semi-automated workflow, followed by manual refinement to ensure highest accuracy. Each dataset entry is enriched with detailed metadata and a subjective quality assessment. With its high level of spatiotemporal precision, FireSpread_MedEU provides essential data for the development and validation of machine learning models or wildfire simulation models. It opens new research opportunities in wildfire behavior analysis, risk assessment, and predictive modeling.…


| Autor*innen: | Simon Müller |
|---|---|
| Koautor*innen: | Anja Hofmann-BöllinghausORCiD, Zhimin Chen, Kristin Vogel, Philipp BennerORCiD |
| Dokumenttyp: | Forschungsdatensatz |
| Veröffentlichungsform: | Graue Literatur |
| Sprache: | Englisch |
| Jahr der Erstveröffentlichung: | 2025 |
| Organisationseinheit der BAM: | 7 Bauwerkssicherheit |
| 7 Bauwerkssicherheit / 7.5 Technische Eigenschaften von Polymerwerkstoffen | |
| VP Vizepräsident | |
| VP Vizepräsident / VP.1 eScience | |
| Herausgeber (Institution): | Bundesanstalt für Materialforschung und -prüfung (BAM) |
| Verlag: | Zenodo |
| Verlagsort: | Geneva |
| DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
| Freie Schlagwörter: | Machine Learning; Spread; Wildfire |
| Themenfelder/Aktivitätsfelder der BAM: | Infrastruktur |
| Infrastruktur / Fire Science | |
| Art der Ressource: | Datensatz |
| Beginndatum der Datenerstellung: | 13.08.2025 |
| DOI: | 10.5281/zenodo.16813435 |
| Beschreibung der Datei(en) : | Daten umfassen Naturbrände in Europa und im gesamten Mittelmeerraum, nachverfolgt mit hochaufgelösten Satellitendaten. |
| Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
| Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
| Datum der Freischaltung: | 19.11.2025 |
| Referierte Publikation: | Nein |
| Schriftenreihen ohne Nummerierung: | Forschungsdatensätze der BAM |


