Benchmarking for practice: Few-shot time-series crop-type classification on the EuroCropsML dataset

Accurate crop-type classification from satellite time series is essential for agricultural monitoring. Consequently, various machine learning (ML) algorithms, aiming on enhancing classification performance on data-scarce tasks, have been developed. While previous evaluations demonstrated the effectiveness of these algorithms in certain situations, these studies frequently lacked real-world scenarios. Hence, the performance of the algorithms in challenging practical applications has not yet been profoundly evaluated. To facilitate future research in this domain, we present the first comprehensive benchmark for evaluating supervised and self-supervised learning (SSL) methods for crop-type classification under real-world conditions. This benchmark study relies on the EuroCropsML time-series dataset, which combines farmer-reported crop data with Sentinel-2 satellite observations from Estonia, Latvia, and Portugal. Our findings indicate that variants of Model-Agnostic Meta-Learning (MAML; Finn et al., 2017) achieve slightly higher accuracy compared to supervised transfer learning and SSL. For instance, algorithms belonging to the MAML-family show on average a 31% higher overall accuracy for a 20-shot benchmark task when compared to regular transfer learning. However, compared to simpler transfer learning, the improvement of meta-learning comes at the cost of increased computational demands and training time. Moreover, supervised methods benefit most when pre-trained and fine-tuned on geographically close regions. In addition, while SSL generally lags behind meta-learning, it demonstrates advantages over training from scratch—particularly in capturing fine-grained features essential for real-world crop-type classification—and also surpasses standard transfer learning. This highlights its practical value when labeled pre-training crop data is scarce. Our insights highlight the trade-offs between accuracy and computational demand in selecting supervised machine learning methods for real-world crop-type classification tasks and underscore the difficulties of knowledge transfer across diverse geographic regions. Furthermore, they demonstrate the practical value of SSL approaches when labeled pre-training crop data is scarce. The corresponding code is publicly available at https://github.com/jsreu/EurocropsML-Meta-Learning and https://github.com/jsreu/EuroCropsML-SSL . • Comprehensive benchmark study on real-world crop-type classification from multi-spectral satellite imagery using the highly granular, multi-class EuroCropsML dataset. • Profound evaluation of regular transfer learning, various meta-learning, as well as self-supervised learning algorithms. • Investigation of transfer and meta-learning algorithms’ capabilities for knowledge transfer across different geographical regions. • In-depth analysis of knowledge transfer (specific to transfer and meta-learning approaches) for crop types that are shared or distinct between different geographical regions.