EuroCropsML 是一个包含 706,683 个欧洲农业用地样本的遥感机器学习数据集，这些样本被分类为 176 种不同的作物类型，旨在为机器学习在作物分类中的应用提供支持，特别是针对少量样本学习场景。该数据集涵盖了欧洲多个地区的农业用地，并包含 2021 年 Sentinel-2 卫星图像的年度时间序列数据，经过预处理以去除云层和其他噪声，确保数据质量。每个数据点都包含 13 个光谱波段的年度时间序列中值像素值，并包含作物类型标签和空间坐标等元数据，这有助于有效地训练和评估分类算法。

🎯 **EuroCropsML 数据集的创建** EuroCropsML 数据集的创建是为了解决现有遥感农业数据集的局限性，例如地理范围有限、包含的作物类型数量有限以及用于训练机器学习模型的标记数据量有限。该数据集包含 706,683 个欧洲农业用地样本，被分类为 176 种不同的作物类型，为机器学习在作物分类中的应用提供了更全面的多类标记数据集，尤其适合少量样本学习场景。 该数据集包含 2021 年 Sentinel-2 卫星图像的年度时间序列数据，经过预处理以去除云层和其他噪声，确保数据质量。每个数据点都包含 13 个光谱波段的年度时间序列中值像素值，并包含作物类型标签和空间坐标等元数据，这有助于有效地训练和评估分类算法。

🚀 **EuroCropsML 数据集的价值** EuroCropsML 数据集的价值在于它能够帮助研究人员更有效地评估机器学习算法，特别是针对少量样本学习场景。该数据集涵盖了欧洲多个地区的农业用地，并包含 176 种不同的作物类型，能够为开发能够准确识别不同地区和条件下作物的机器学习模型提供更全面的数据。 研究人员已经使用 EuroCropsML 数据集进行了一些初始实验，结果表明模型的性能显著提高。例如，在 500 次样本学习场景中，在拉脱维亚数据上预训练的模型的准确率达到了 0.66，显著优于没有预训练的模型，后者只有 0.28 的准确率。尽管葡萄牙的气候和作物类型不同，但使用该数据进行预训练也提高了模型的性能，尽管提升幅度较小。这突出了迁移学习的价值以及多样化训练数据在提高模型准确率方面的重要性。

💡 **EuroCropsML 数据集的应用** EuroCropsML 数据集的应用可以帮助研究人员开发更准确的作物分类模型，从而更好地了解全球农业用地的分布情况，并为农业管理、粮食安全和环境监测提供更准确的信息。 该数据集的创建也为机器学习在遥感领域的应用提供了新的可能性，例如，可以利用该数据集开发能够预测作物产量、监测作物生长状况以及识别作物病害的机器学习模型。

🌍 **EuroCropsML 数据集的意义** EuroCropsML 数据集的意义在于它为机器学习在农业领域的研究提供了一个新的工具，能够帮助研究人员开发更准确、更有效的作物分类模型。这将有助于提高全球农业的生产效率、可持续性和应对气候变化的能力。 EuroCropsML 数据集的创建也体现了遥感技术在农业领域中的重要作用，以及机器学习在解决全球粮食安全问题方面所发挥的越来越重要的作用。

Remote sensing is a crucial field utilizing satellite and aerial sensor technologies to detect and classify objects on Earth, playing a significant role in environmental monitoring, agricultural management, and natural resource conservation. These technologies enable scientists to gather extensive data over vast geographic areas and periods, providing insights essential for informed decision-making. Monitoring agricultural crop distribution worldwide is particularly important for food security, a core Sustainable Development Goal of the United Nations. With five billion hectares of agricultural land globally, accurate crop type classification is essential for managing farming practices and ensuring food production meets the needs of growing populations.

A main challenge in remote sensing for agriculture is accurately classifying crop types across diverse regions. Traditional datasets are often limited by their geographical scope, the number of crop types included, and the volume of labeled data available for training machine learning models. These limitations hinder the effective benchmarking of machine learning algorithms, especially those using few-shot learning techniques, which require models to perform well with few examples. Consequently, there is a pressing need for more comprehensive datasets that cover various geographic regions and crop types, allowing for better algorithm development and research comparability.

Existing methods for crop type classification rely on various datasets like ZUERICROP for northern Switzerland, BREIZHCROPS for the French Brittany region, and CROP HARVEST, a global dataset mainly featuring binary crop-vs.-non-crop labels. However, these datasets are restricted to small areas within a single country or include a limited number of agricultural parcels, making them less effective for broad benchmarking purposes. For instance, CROP HARVEST contains data from 116,000 parcels globally, but only a small fraction of this data is multi-class labeled, limiting its utility for developing sophisticated classification models.

Researchers from the Technical University of Munich, dida Datenschmiede GmbH,  ETH Zürich, and Zuse Institute Berlin have introduced the EUROCROPSML dataset to address these limitations. This dataset comprises 706,683 European agricultural parcels, classified into 176 distinct crop types. The dataset is designed to support advancements in machine learning for crop classification by providing a comprehensive, multi-class labeled dataset suitable for few-shot learning. This large and diverse dataset facilitates the development of robust machine-learning models that can accurately classify crops across different regions and conditions.

The EUROCROPSML dataset includes annual time series data of median pixel values from Sentinel-2 satellite imagery for 2021. The data is meticulously pre-processed to remove cloud cover and other noise, ensuring high-quality input for machine learning models. Each data point is represented by a time series of median pixel values for each of the 13 spectral bands of the Sentinel-2 imagery, providing detailed information on the light reflected by the Earth’s surface across various wavelengths. This dataset also includes essential metadata, such as crop type labels and spatial coordinates, which facilitates effective training and evaluation of classification algorithms.

Initial experiments with the EUROCROPSML dataset demonstrated significant improvements in model performance. For instance, models pre-trained on Latvian data achieved an accuracy of 0.66 in a 500-shot learning scenario, significantly outperforming models without pre-training, which only achieved an accuracy of 0.28. The incorporation of data from Portugal, despite its different climate and crop types, further improved performance, though less dramatically. This highlights the value of transfer learning and the importance of diverse training data in enhancing model accuracy.

In conclusion, the EUROCROPSML provides a comprehensive and well-structured dataset that enables more effective benchmarking of machine learning algorithms, particularly for few-shot learning. This dataset, which includes data from 706,683 agricultural parcels across Europe and covers 176 crop types, is poised to enhance crop type classification across diverse regions. The initial results are promising, with models pre-trained on this dataset demonstrating superior performance in classifying crops accurately.

---

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter..

Don’t Forget to join our 47k+ ML SubReddit

Find Upcoming AI Webinars here

The post EuroCropsML: An Analysis-Ready Remote Sensing Machine Learning Dataset for Time Series Crop Type Classification of Agricultural Parcels in Europe appeared first on MarkTechPost.