Gaining access to labeled reference data is one of the great challenges in supervised machine-learning endeavors. This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges, such as urbanization and climate change, using state-of-the-art machine-learning techniques. To meet these pressing needs, especially in urban research, we provide open access to a valuable benchmark data set, So2Sat LCZ42, which consists of local-climate-zone (LCZ) labels of approximately half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe.

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So2Sat LCZ42：全球地方气候区分类的基准数据集

获得对标记参考数据的访问权限是监督机器学习工作中的一大挑战。对于在全球范围内对遥感图像进行自动分析尤其如此，这使我们能够使用最先进的机器学习技术应对全球挑战，例如城市化和气候变化。为了满足这些紧迫的需求，尤其是在城市研究中，我们提供了对有价值的基准数据集 So2Sat LCZ42 的开放访问，该数据集由大约 50 万个 Sentinel-1 和 Sentinel-2 图像的本地气候区 (LCZ) 标签组成分布在全球 42 个城市群（加上 10 个额外的较小区域）。