EuroCropsML

EuroCropsML is a ready-to-use ML dataset combining EuroCrops (v9) 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:

1. 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).
2. 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, fine-tuning on Estonia
• latvia_vs_estonia: pre-training on Latvia and fine-tuning on Estonia

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 2: Remove data points that contain only clouds after pre-processing.
• Version 1: Initial publication