OSMlanduse a dataset of European Union land use at 10 m resolution derived from OpenStreetMap and Sentinel-2
Our map represents the first successful large-area fusion of OpenStreetMap and Copernicus data at a spatial resolution of 10 m or finer and can be applied globally. We addressed varying label noise and feature space quality, utilizing artificial intelligence and advanced computing. Our method relies...
Gespeichert in:
| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
06 May 2025
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| In: |
Scientific data
Year: 2025, Jahrgang: 12, Pages: 1-7 |
| ISSN: | 2052-4463 |
| DOI: | 10.1038/s41597-025-04703-8 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41597-025-04703-8 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41597-025-04703-8 
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| Verfasserangaben: | Michael Schultz, Hao Li, Zhaoyan Wu, Daniel Wiell, Michael Auer, Alexander Zipf |
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| 245 | 1 | 0 | |a OSMlanduse a dataset of European Union land use at 10 m resolution derived from OpenStreetMap and Sentinel-2 |c Michael Schultz, Hao Li, Zhaoyan Wu, Daniel Wiell, Michael Auer, Alexander Zipf |
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| 520 | |a Our map represents the first successful large-area fusion of OpenStreetMap and Copernicus data at a spatial resolution of 10 m or finer and can be applied globally. We addressed varying label noise and feature space quality, utilizing artificial intelligence and advanced computing. Our method relies solely on openly available data streams and methods, eliminating training data acquisition or the need for additional expert knowledge for such purpose. We extracted land use labels from OpenStreetMap and remote sensing data to create a contiguous land use map of the European Union as of March 2020. OpenStreetMap tags were translated into land use labels, directly mapping 61.8% of the Union’s area. These labels served as training data for a classification model, predicting land use in remaining areas. Country-specific deep learning convolutional neural networks and Sentinel-2 feature space composites of 2020 at 10 m resolution were employed. The overall map accuracy is 89%, with class-specific accuracies ranging from 77% to 99%. The data set is available for download from https://doi.org/10.11588/data/IUTCDNand visualization at https://osmlanduse.org. | ||
| 650 | 4 | |a Environmental impact | |
| 650 | 4 | |a Geography | |
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