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

arXiv: https://arxiv.org/abs/2407.17458 · HTML: https://mlsanity.com/paper/2407.17458/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Crop mapping from image time series: deep learning with multi-scale label hierarchies](https://mlsanity.com/paper/2102.08820/) (2021)
- [BreizhCrops: A Time Series Dataset for Crop Type Mapping](https://mlsanity.com/paper/1905.11893/) (2019)
- [There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation](https://mlsanity.com/paper/2310.19231/) (2023)
- [EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification](https://mlsanity.com/paper/2106.08151/) (2021)

## Built on by (most cited first, 11 of 11)

- [GTPBD: A Fine-Grained Global Terraced Parcel and Boundary Dataset](https://mlsanity.com/paper/2507.14697/) (2025)
- [A Comprehensive Review of Agricultural Parcel and Boundary Delineation from Remote Sensing Images: Recent Progress and Future Perspectives](https://mlsanity.com/paper/2508.14558/) (2025)
- [Benchmarking Geospatial Foundation Models for Agriculture Applications](https://mlsanity.com/paper/2606.29664/) (2026)
- [SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping](https://mlsanity.com/paper/2608.09497/) (2026)
- [Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset](https://mlsanity.com/paper/2504.11022/) (2025)
- [T3S: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series](https://mlsanity.com/paper/2506.12885/) (2025)
- [Farm-Level, In-Season Crop Identification for India](https://mlsanity.com/paper/2507.02972/) (2025)
- [The Normalized Difference Layer: A Differentiable Spectral Index Formulation for Deep Learning](https://mlsanity.com/paper/2601.06777/) (2026)
- [Retrieval-Augmented Multi-scale Framework for County-Level Crop Yield Prediction Across Large Regions](https://mlsanity.com/paper/2603.07305/) (2026)
- [DirPA: Addressing Prior Shift in Imbalanced Few-shot Crop-type Classification](https://mlsanity.com/paper/2603.12905/) (2026)
- [TIDES: Implicit Time-Awareness in Selective State Space Models](https://mlsanity.com/paper/2605.09742/) (2026)

## Abstract (arXiv)

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.