Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset

• URL: http://arxiv.org/abs/2504.11022v2
• Date: Thu, 25 Sep 2025 13:37:34 GMT
• Title: Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset
• Authors: Joana Reuss, Jan Macdonald, Simon Becker, Ekaterina Gikalo, Konrad Schultka, Lorenz Richter, Marco Körner,
• Abstract summary: MAML-based meta-learning algorithms achieve slightly higher accuracy compared to supervised transfer learning and SSL methods.<n> supervised methods benefit most when pre-trained and fine-tuned on geographically close regions.<n> SSL demonstrates advantages over training from scratch, particularly in capturing fine-grained features essential for real-world crop-type classification.
• Score: 11.040288800003054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate crop-type classification from satellite time series is essential for agricultural monitoring. While various machine learning algorithms have been developed to enhance performance on data-scarce tasks, their evaluation often lacks real-world scenarios. Consequently, their efficacy in challenging practical applications has not yet been profoundly assessed. To facilitate future research in this domain, we present the first comprehensive benchmark for evaluating supervised and 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 MAML-based meta-learning algorithms achieve slightly higher accuracy compared to supervised transfer learning and SSL methods. 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 underscore the trade-offs between accuracy and computational demand in selecting supervised machine learning methods for real-world crop-type classification tasks and highlight 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.

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