RapidCrops: A pan-European label dataset for large-scale crop classification

99M agricultural parcel boundaries with harmonised crop type information across a wide spatio-temporal coverage; covering seven EU countries for 5-7 years. Data is derived from parcel boundaries and crop type information reported under the EU IACS programme. Our methodology seeks to improve the usability of the parcel boundaries without diluting their integrity. Additional attributes are provided to support the use of the data in ML workflows; especially for those leveraging EO data. This dataset builds on top of significant prior contributions, namely the EuroCrops crop dataset harmonisation initiative and the fiboa open data standard for parcel boundaries.

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Visibility: Public
Owner: Planet
Created: 24 Mar 2025
Last Updated: 21 Aug 2025

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RapidCrops

Data coverage

Banner image showing geographical extent of the dataset.

Dense temporal coverage (2018-2022) across seven countries:

Austria
Denmark
Spain (partial coverage in Catalonia only)
France
Germany (partial coverage in Brandenberg, Nordrhein Westfalen & Lower Saxony)
Netherlands
Portugal

Additional years of coverage are included for:

Germany (2023 & 2024)
France (2023)
Netherlands (2023 & 2024)

Datasets are published in two forms:

"National": National datasets are provided individually for each year of coverage.
"Combined": All national datasets are combined into a single partitioned (by country) dataset for a given year.

Data structure

Because all data resides within Europe, boundary geometries are all provided in EPSG:3035.

See below an example GeoParquet schema for the France 2020 dataset:

Usability and efficiency

Redundant vertices were removed from the boundary polygons with a tolerance of 20cm to protect the integrity of the parcel. This resulted in a >25% reduction in the number of vertices, allowing for more efficient spatial queries.

Assessing suitability for combining parcels with EO products

Identifying parcels from which clean pixels can be obtained from satellite imagery depends on both the size of a parcel and the regularity of its shape. RapidCrops provides attributes to help quantify these properties:

parcel_width: The length of the shortest edge on the parcel boundary's minimum rotated rectangle
area_ratio_hull: The ratio between the area of the parcel boundary, and that of its convex hull
max_contained_radius: The radius of the parcel boundary's maximum inscribed circle Knowing this can help to filter out parcels from which meaningful analysis cannot be performed, or to help explain variation in performance across parcels.

These metrics can be used to identify suitable parcels for analysis by a given EO sensor, as highlighted below:

Facilitating spatial stratification

Grid cell IDs from two popular spatial gridding systems (Quadkey & H3) are included for each boundary to allow for spatially-stratified sampling. The gridding systems are hierarchical and coarser grid resolutions can be derived form the values provided according to the user’s need.

Below is the quadkey spatial gridding at the resolution provided:

But coarser resolution gridding can be derived by reading a subset of the leftmost characters in the key:

Accessing the data

Underlying data licenses

The data is made available subject to the licenses associated with the underlying source datasets. No additional limitations or conditions are applied. See below for links to the underlying license information for each source dataset:

Source dataset	Dataset license
Austria	INSPIRE public access license & CC-BY-AT 4.0
Denmark	INSPIRE public access license & INSPIRE no conditions & CC0 1.0 Universal
France	Custom open license
Germany	Custom open licenses: NRW, Brandenberg, LS
Netherlands	INSPIRE public access license & INSPIRE no conditions & Dutch creative commons license
Portugal	CC BY 4.0
Spain	Custom open license

Suggested citation

Holden, P., Davis, T., Holmes, C., Senaras, C., & Wania, A. (2025). RapidCrops: A pan-European label dataset for large-scale crop classification (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15166359

In BibTeX format:

1@dataset{rapidcrops,
2  title = {RapidCrops: A pan-European label dataset for large-scale crop classification},
3  publisher = {Zenodo},
4  doi = {https://doi.org/10.5281/zenodo.15166359},
5  year = {2025},
6  urldate = {<date of access, ISO>},
7  author = {
8    Holden, Piers and
9    Davis, Timothy and
10    Holmes, Christopher and
11    Senaras, Caglar and
12    Wania, Annett
13  },
14  version = {1.0},
15}

1@dataset{rapidcrops,
2  title = {RapidCrops: A pan-European label dataset for large-scale crop classification},
3  publisher = {Zenodo},
4  doi = {https://doi.org/10.5281/zenodo.15166359},
5  year = {2025},
6  urldate = {<date of access, ISO>},
7  author = {
8    Holden, Piers and
9    Davis, Timothy and
10    Holmes, Christopher and
11    Senaras, Caglar and
12    Wania, Annett
13  },
14  version = {1.0},
15}

1import geopandas as gpd
2
3url = "s3://planet/rapidcrops/national/AUT/rapidcrops_2018_aut.geo.parquet"
4
5# Retrieve a full dataset
6df = gpd.read_parquet(url)
7
8# OR, filter the dataset on a bbox for faster retrieval
9df = gpd.read_parquet(url, bbox=bbox)

1import geopandas as gpd
2
3url = "s3://planet/rapidcrops/national/AUT/rapidcrops_2018_aut.geo.parquet"
4
5# Retrieve a full dataset
6df = gpd.read_parquet(url)
7
8# OR, filter the dataset on a bbox for faster retrieval
9df = gpd.read_parquet(url, bbox=bbox)