3D-GloBFP: the first global three-dimensional building footprint dataset (PART Ⅲ, grid ID: 700-899)

The 3D Global Building Footprints (3D-GloBFP) dataset is the first global-scale building height dataset at the individual building footprint level for the year 2020, generated through the integration of multisource Earth Observation (EO) data and the extreme gradient boosting (XGBoost) model. The reliability and accuracy of 3D-GloBFP have been validated across 33 subregions, achieving R² values ranging from 0.66 to 0.96 and root-mean-square errors (RMSEs) between 1.9 m and 14.6 m. The dataset is divided into spatial grid-based tiles, each stored as an individual ShapeFile (.shp) containing estimated building heights (in meters) in attribute tables. See world_grid.shp and readme.txt at https://doi.org/10.5281/zenodo.11319912 for grid partitioning and naming details.

3D全球建筑足迹（3D Global Building Footprints, 3D-GloBFP）数据集是首个针对2020年、基于单体建筑足迹级别的全球尺度建筑高度数据集，通过融合多源地球观测（Earth Observation, EO）数据与极端梯度提升（extreme gradient boosting, XGBoost）模型生成。该数据集的可靠性与准确性已在33个分区完成验证，其决定系数（R²）取值范围为0.66至0.96，均方根误差（root-mean-square error, RMSE）介于1.9米至14.6米之间。数据集按空间网格分块存储，每个分块以独立的形状文件（ShapeFile, .shp）形式保存，属性表中包含以米为单位的估算建筑高度。有关网格划分与命名的详细信息，请参阅https://doi.org/10.5281/zenodo.11319912 中的world_grid.shp与readme.txt文件。