Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5357-2024
https://doi.org/10.5194/essd-16-5357-2024
Data description paper
 | 
25 Nov 2024
Data description paper |  | 25 Nov 2024

3D-GloBFP: the first global three-dimensional building footprint dataset

Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai

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Cited articles

Arehart, J., Pomponi, F., D'Amico, B., and Srubar III, W.: A new estimate of building floor space in North America, Environ. Sci. Technol., 55, 5161–5170, https://doi.org/10.1021/acs.est.0c05081, 2021. 
Arehart, J. H., Pomponi, F., D'Amico, B., and Srubar, W. V.: Structural material demand and associated embodied carbon emissions of the United States building stock: 2020–2100, Resour. Conserv. Recy., 186, 106583, https://doi.org/10.1016/j.resconrec.2022.106583, 2022. 
Basaraner, M. and Cetinkaya, S.: Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS, Int. J. Geogr. Inf. Sci., 31, 1952–1977, https://doi.org/10.1080/13658816.2017.1346257, 2017. 
Cai, B., Shao, Z., Huang, X., Zhou, X., and Fang, S.: Deep learning-based building height mapping using Sentinel-1 and Sentinel-2 data, Int. J. Appl. Earth Obs., 122, 103399, https://doi.org/10.1016/j.jag.2023.103399, 2023. 
Cao, Y. and Huang, X.: A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities, Remote Sens. Environ., 264, 112590, https://doi.org/10.1016/j.rse.2021.112590, 2021. 
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Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
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