3D-GloBFP: the first global three-dimensional building footprint dataset (PART Ⅶ, grid ID: 1800-1899)

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.

三维全球建筑轮廓数据集（3D Global Building Footprints, 3D-GloBFP）是首个针对2020年、基于单体建筑轮廓级别的全球尺度建筑高度数据集，通过多源地球观测（Earth Observation, EO）数据与极限梯度提升（extreme gradient boosting, XGBoost）模型融合生成。该数据集的可靠性与准确性已在33个分区中得到验证，决定系数（R²）取值范围为0.66至0.96，均方根误差（root-mean-square errors, RMSE）介于1.9米至14.6米之间。数据集按空间网格分幅存储，每个分幅以独立的形状文件（ShapeFile, .shp）格式保存，属性表中包含以米为单位的估算建筑高度数值。如需了解网格划分与命名的详细细则，请参阅https://doi.org/10.5281/zenodo.11319912中提供的world_grid.shp与readme.txt文件。