
doi: 10.5281/zenodo.12168505 , 10.5281/zenodo.10629609 , 10.5281/zenodo.10887070 , 10.5281/zenodo.13789558 , 10.5281/zenodo.10818792 , 10.5281/zenodo.10816953 , 10.5281/zenodo.15095445 , 10.5281/zenodo.15006273 , 10.5281/zenodo.14161939 , 10.5281/zenodo.10629610 , 10.5281/zenodo.10804243 , 10.5281/zenodo.10683255
doi: 10.5281/zenodo.12168505 , 10.5281/zenodo.10629609 , 10.5281/zenodo.10887070 , 10.5281/zenodo.13789558 , 10.5281/zenodo.10818792 , 10.5281/zenodo.10816953 , 10.5281/zenodo.15095445 , 10.5281/zenodo.15006273 , 10.5281/zenodo.14161939 , 10.5281/zenodo.10629610 , 10.5281/zenodo.10804243 , 10.5281/zenodo.10683255
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.
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