欧洲欧洲农作物时间序列基准数据集 (EUROCROPSML) 用于欧洲少数作物类型的分类
Joana Reuss1, Jan Macdonald2, Simon Becker2,3
1Technical University of Munich, Chair of Remote Sensing Technology, Munich, 80333, Germany. joana.reuss@tum.de.
Scientific data
|April 19, 2025
概括
我们介绍 EUROCROPSML,这是一个新的遥感数据集,用于对机器学习 (ML) 在欧洲作物分类进行比较. 这一时间解析数据集有助于开发和比较跨国少数射击学习算法.
科学领域:
- 地球观测 地球观测
- 机器学习 机器学习
- 农业科学 农业科学
背景情况:
- 农作物类型的分类对于农业监测和粮食安全至关重要.
- 现有的遥感数据集往往缺乏先进机器学习所需的时间分辨率和跨国范围.
- 对作物分类的机器学习算法进行基准测试需要标准化,分析准备的数据.
研究的目的:
- 推出EUROCROPSML,一个分析准备的遥感数据集,用于对欧洲的时间序列作物类型分类进行基准测试.
- 提供一个标准化的数据集,用于评估跨国少数射击学习算法.
- 促进在农业遥感领域的算法开发和研究可比性的进步.
主要方法:
- 利用开源的 EUROCROPS 集合来创建一个时间解析的数据集.
- 从Sentinel-2 L1C数据中提取每块的中间像素值.
- 结合精确的地理空间坐标和176种作物类别的多类标签.
主要成果:
- 创建 EUROCROPSML,包括 706,683 个多类标记数据点.
- 数据集包括Sentinel-2 L1C数据和地理空间坐标的时间序列.
- 该数据集在Zenodo上公开提供,供研究使用.
结论:
- EUROCROPSML是欧洲基于机器学习的作物类型分类的基础资源.
- 该数据集能够在跨国背景下对少数射击学习算法进行可靠的基准测试.
- 欧罗克罗普斯ML的可用性促进了可复制的研究,并加速了农业情报的进步.
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