EuroCropsML: A Time Series Benchmark Dataset For Few-Shot Crop Type Classification

TL;DR

EuroCropsML is a comprehensive, open-source dataset designed to facilitate the development and benchmarking of few-shot crop type classification algorithms across Europe using satellite time series data.

Contribution

It introduces the first transnational benchmark dataset for few-shot crop classification, enabling standardized evaluation and comparison of algorithms in this domain.

Findings

Supports transnational crop classification research

Contains 706,683 labeled data points across 176 classes

Enables development of few-shot learning algorithms

Abstract

We introduce EuroCropsML, an analysis-ready remote sensing machine learning dataset for time series crop type classification of agricultural parcels in Europe. It is the first dataset designed to benchmark transnational few-shot crop type classification algorithms that supports advancements in algorithmic development and research comparability. It comprises 706 683 multi-class labeled data points across 176 classes, featuring annual time series of per-parcel median pixel values from Sentinel-2 L1C data for 2021, along with crop type labels and spatial coordinates. Based on the open-source EuroCrops collection, EuroCropsML is publicly available on Zenodo.