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Vineyard Map Service

The Vineyard Map Service provides the vineyard area delimitation of the Spanish AoI through time-series analysis and the application of a Machine Learning (ML) model designed to extract vineyards as the crop type of interest for this AoI. The ML model is pre-trained, and the vine

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CreatorPérez Ramirez, Patricia
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Published2026-05-13
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DOI10.5281/zenodo.20155572
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Downloads32
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Licensecc-by-4.0
File Size33.7 MB
Data TypeDataset
Published2026
Licensecc-by-4.0
Total Views56
Total Downloads32

The Vineyard Map Service provides the vineyard area delimitation of the Spanish AoI through time-series analysis and the application of a Machine Learning (ML) model designed to extract vineyards as the crop type of interest for this AoI. The ML model is pre-trained, and the vineyard mapping service is executed in inference mode only. The service only operates using high-resolution (HR) data processed from Sentinel-1 and Sentinel-2 missions. 

This service is part of the set of Earth Observation (EO) services developed in order to illustrate, test, validate and demonstrate the LEONSEGS capabilities. The defined use cases are implemented and tested in two Areas of Interest (AoIs), located in Spain and Austria. This service is only provided for the Spanish use case, focused on vineyard monitoring.

This Vineyard Map is an example of the outputs produced by the LEONSEGS system in response to a user request.

Outputs: 
• Vineyard map accompanied by a per-pixel uncertainty map. 

For the each product, the following files are provided:
      – GeoTIFF raster containing the original values
      – Categorized GeoTIFF raster
      – RGB visualization GeoTIFF
      – PNG legend associated with the RGB visualization
      – Raster footprint geometry (.wkt)
      – ISO 19115/19139 metadata (.xml)
      – STAC metadata (.geojson)

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Vineyard Map Service (Full Dataset)33.7 MB
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ReadmeVia DOI record
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Files are hosted on the source repository. Click download to access the full dataset.

Pérez Ramirez, Patricia (2026). Vineyard Map Service. https://doi.org/10.5281/zenodo.20155572