EUNIS habitat maps: enhancing thematic and spatial resolution for Europe through machine learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F25%3A00143473" target="_blank" >RIV/00216224:14310/25:00143473 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1038/s41597-025-06235-7" target="_blank" >https://doi.org/10.1038/s41597-025-06235-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41597-025-06235-7" target="_blank" >10.1038/s41597-025-06235-7</a>
Alternative languages
Result language
angličtina
Original language name
EUNIS habitat maps: enhancing thematic and spatial resolution for Europe through machine learning
Original language description
The EUNIS habitat classification is crucial for categorising European habitats, supporting European policy on nature conservation and implementing the Nature Restoration Law. To meet the growing demand for detailed and accurate habitat information, we provide spatial predictions across Europe (EEA39 territory) for 260 EUNIS habitat types at hierarchical level 3, together with independent validation and uncertainty analyses. Using ensemble machine learning models, together with high-resolution satellite imagery and ecologically meaningful climatic, topographic and edaphic variables, we produced a European habitat map indicating the most probable habitat overall at 100-m resolution across Europe. Additionally, we provide information on prediction uncertainty and the most probable habitats at level 3 within each EUNIS level 1 formation. This product is particularly useful for both conservation and restoration purposes. Predictions were cross-validated at European scale using a spatial block cross-validation and evaluated against independent data from France (forests only), the Netherlands and Austria. The maps achieved strong predictive performance, with F1-scores ranging from 0.61 to 0.94 in spatial cross-validation and from 0.33 to 0.95 in external validation datasets with distinct trade-offs in terms of recall and precision across habitat formations. Accuracy improved for rare or localized habitats when considering the top 3 predicted classes.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10600 - Biological sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Data
ISSN
2052-4463
e-ISSN
2052-4463
Volume of the periodical
12
Issue of the periodical within the volume
1
Country of publishing house
DE - GERMANY
Number of pages
18
Pages from-to
1-18
UT code for WoS article
001642806600001
EID of the result in the Scopus database
2-s2.0-105025454413