论文标题
Landcovernet：全球基准土地覆盖分类培训数据集
LandCoverNet: A global benchmark land cover classification training dataset
论文作者
论文摘要
定期更新和准确的土地覆盖地图对于监视17个可持续发展目标中的14个至关重要。多光谱卫星图像在全球规模上提供了高质量和有价值的信息，可用于开发土地覆盖分类模型。但是，这样的全球应用需要地理上多样化的培训数据集。在这里，我们提出了LandCovernet，这是一个基于Sentinel-2观测值的全球培训数据集，该数据集以10m的空间分辨率为基础。土地覆盖类标签是根据Sentinel-2的年度时间序列定义的，并通过三个人类注释者之间的共识来验证。
Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and valuable information at global scale that can be used to develop land cover classification models. However, such a global application requires a geographically diverse training dataset. Here, we present LandCoverNet, a global training dataset for land cover classification based on Sentinel-2 observations at 10m spatial resolution. Land cover class labels are defined based on annual time-series of Sentinel-2, and verified by consensus among three human annotators.
