# EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification

arXiv: https://arxiv.org/abs/2106.08151 · HTML: https://mlsanity.com/paper/2106.08151/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [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)

## Built on by (most cited first, 13 of 13)

- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding](https://mlsanity.com/paper/2203.09175/) (2022)
- [Deep Learning for Satellite Image Time Series Analysis: A Review](https://mlsanity.com/paper/2404.03936/) (2024)
- [EuroCropsML: A Time Series Benchmark Dataset For Few-Shot Crop Type Classification](https://mlsanity.com/paper/2407.17458/) (2024)
- [Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi's domain adaptability](https://mlsanity.com/paper/2409.00489/) (2024)
- [Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data](https://mlsanity.com/paper/2305.12011/) (2023)
- [Annual field-scale maps of tall and short crops at the global scale using GEDI and Sentinel-2](https://mlsanity.com/paper/2212.09681/) (2022)
- [Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset](https://mlsanity.com/paper/2504.11022/) (2025)
- [Harnessing Administrative Data Inventories to Create a Reliable Transnational Reference Database for Crop Type Monitoring](https://mlsanity.com/paper/2310.06393/) (2023)
- [XAI for Early Crop Classification](https://mlsanity.com/paper/2310.06574/) (2023)
- [DirPA: Addressing Prior Shift in Imbalanced Few-shot Crop-type Classification](https://mlsanity.com/paper/2603.12905/) (2026)
- [LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift](https://mlsanity.com/paper/2605.18541/) (2026)
- [Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality](https://mlsanity.com/paper/2608.05979/) (2026)

## Abstract (arXiv)

We present EuroCrops, a dataset based on self-declared field annotations for training and evaluating methods for crop type classification and mapping, together with its process of acquisition and harmonisation. By this, we aim to enrich the research efforts and discussion for data-driven land cover classification via Earth observation and remote sensing. Additionally, through inclusion of self-declarations gathered in the scope of subsidy control from all countries of the European Union (EU), this dataset highlights the difficulties and pitfalls one comes across when operating on a transnational level. We, therefore, also introduce a new taxonomy scheme, HCAT-ID, that aspires to capture all the aspects of reference data originating from administrative and agency databases. To address researchers from both the remote sensing and the computer vision and machine learning communities, we publish the dataset in different formats and processing levels.