EuroCropsML

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 (parcel's centroid in [lon,lan]; observation dates). In addition, we performed some cloud removal steps. Each .npz-file is saved with the following naming convention: NUTS3region_parcelID_EC_hcat_c.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, from 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, from which 46 have not been seen during pre-training) For both use cases, the fine-tuning split is as follows: train: 1-, 5-, and 10-shot (for few-shot classification and benchmarking) validation: 1000 samples test: all samples Changelog Version 3: Some parcels have been clipped incorrectly. Please do not rely on this version. Use version 4 or newer. Version 2: Remove datapoints that contain only cloudy observations (in preprocess.zip). Version 1: Initial publication