With leaps in machine learning techniques and their applicationon Earth observation challenges has unlocked unprecedented performance across the domain. While the further development of these methods was previously limited by the availability and volume of sensor data and computing resources, the lack of adequate reference data is now constituting new bottlenecks. Since creating such ground-truth information is an expensive and error-prone task, new ways must be devised to source reliable, high-quality reference data on large scales. As an example, we showcase E URO C ROPS, a reference dataset for crop type classification that aggregates and harmonizes administrative data surveyed in different countries with the goal of transnational interoperability.

翻译：随着机器学习技术的突破及其在地球观测挑战中的应用，该领域实现了前所未有的性能提升。尽管此前这些方法的进一步发展受限于传感器数据与计算资源的可用性及规模，但当前缺乏充分参考数据已成为新的瓶颈。由于创建此类地面实况信息是一项成本高昂且易出错的任务，必须设计新途径来大规模获取可靠、高质量的参考数据。作为示例，我们展示了EUROCROPS——一个通过聚合和统一不同国家行政调查数据、以跨国互操作为目标的作物类型分类参考数据集。