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全球建筑轮廓（3D-GloBFP）数据集是首个面向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精度已在33个分区中得到验证，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。该数据集按空间网格分幅存储，每幅均为独立的ShapeFile格式（.shp）文件，其属性表中包含以米为单位的估算建筑高度数值。有关网格划分与命名规则的详细信息，请参阅https://doi.org/10.5281/zenodo.11319912处的world_grid.shp与readme.txt文件。

搜集汇总

背景与挑战

背景概述

3D-GloBFP是全球首个2020年全球尺度的三维建筑足迹数据集，基于多源地球观测数据和XGBoost模型生成，具有较高的验证精度（R² 0.66-0.96，RMSE 1.9-14.6米）。数据集以空间网格分块存储，每个块为ShapeFile文件，适用于城市分析、景观生态学等领域。