Powered by OpenAIRE graph
Found an issue? Give us feedback
ZENODOarrow_drop_down
ZENODO
Dataset . 2024
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY SA
Data sources: Datacite
versions View all 12 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

EuroCropsML

Abstract

EuroCropsML* is a ready-to-use ML dataset combining EuroCrops reference data with Sentinel-2 reflectance data from 2021. It contains data from Latvia, Portugal, and Estonia and is intended for benchmarking few-shot crop type classification. We used Eurostat's GISCO dataset to map the EuroCrops parcels to their NUTS1-3 region. The provided data comes in two stages: raw_data.zip (stage 1): One dataframe per country containing a annual time series of observations for each parcel, as well as separate files for the parcels' geometries and classes (EC_hcat_c = 10-digit HCAT code indicating the hierarchy of the crop). preprocess.zip (stage 2): Read-to-use .npz-files. Each data point is saved in an .npz-file along with its metadata. In addition, we performed some cloud removal steps. Each .npz-file is saved with the following naming convention: __.npz Furthermore, split.zip contains .json-files that split the files from preprocess.zip into a pre-training/meta-learning (train and validation) and fine-tuning (train, validation, and test) dataset. In total, we provide two use cases: latvia_portugal_vs_estonia: pre-training on Latvia and Portugal (142 distinct classes), fine-tuning on Estonia (127 distinct classes, of which 34 have not been seen during pre-training) latvia_vs_estonia: pre-training on Latvia (103 distinct classes) and fine-tuning on Estonia (127 distinct classes, of which 46 have not been seen during pre-training) For both use cases, the fine-tuning split is as follows: train: 1-, 5-, 10-, 20-, 100-, 200-, 500-shot (for few-shot classification and benchmarking) and all samples validation: 1000 samples test: all samples Changelog Version 7: Added new few-shot fine-tuning splits: 200 and 500 Version 6: Added new (few-shot) fine-tuning splits: 20, 100, and all samples Version 4: The EuroCrops shapefilees sometimes contain a couple of parcels that lie outside the national borders. We now map them to the closest NUTS region within the country. Please rely on this version or newer. Version 3: Some parcels have been clipped incorrectly. Version 2: Remove datapoints that contain only cloudy observations (in preprocess.zip). Version 1: Initial publication * Contains Copernicus Sentinel data (2024), processed on EOLab Country-secific data sources for EuroCrops reference data Estonia: INSPIRE GEOPORTAL If link does not work, search for Estonia --> Geospatial Aid Application Estonia Agricultural parcels on the INSPIRE platform. Latvia: Lauku atbalsta dienests Updated Source Portugal: Download via WFS https://www.ifap.pt/isip/ows/isip.data/wfs or over the IFAP website.

Related Organizations
  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    1
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average