Global Gridded Building Heights for Cities

Building height is recognized as a critical variable for accurately modeling the relationship between urban density and carbon emissions.

This data repository contains pre-processed building height for cities obtained from Che., et al (2024). We re-aggregate the global shapefiles to match Global Human Settlement Layers global tiles.

The predicted building heights achieve robust predictive performance with reported R^2 values between 0.66 and 0.96 and root-mean-square errors (RMSE) ranging from 1.9 to 14.6 meters across 33 subregions

Additionally, the selected dataset closely aligns with manually validated reference datasets provided by established entities such as ONEGEO Map, Baidu Maps, the United States Geological Survey (USGS), Microsoft Building Heights, and EMU Analytics (England).

Citation:

Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Zhang, H., Yuan, H., and Dai, Y.: 3D-GloBFP: the first global three-dimensional building footprint dataset, Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, 2024.