Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-5357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 Nov 2024
Data description paper |
| 25 Nov 2024
| 25 Nov 2024
3D-GloBFP: the first global three-dimensional building footprint dataset
Yangzi Che
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Xuecao Li
College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
Xiaoping Liu
CORRESPONDING AUTHOR
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Yuhao Wang
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Weilin Liao
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Xianwei Zheng
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China
Xucai Zhang
Department of Geography, Ghent University, 9000 Ghent, Belgium
Xiaocong Xu
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Qian Shi
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Jiajun Zhu
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Honghui Zhang
School of Geographical Sciences and Remote Sensing, Guangzhou University, Guangzhou, 510006, China
Guangdong Engineering Center for Intelligent Spatial Planning, Guangdong Guodi Planning Science Technology Co. Ltd, Guangzhou, 510651, China
Hua Yuan
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, 510275, China
Yongjiu Dai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, 510275, China
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Cited
6 citations as recorded by crossref.
- How Does the Urban Built Environment Affect the Accessibility of Public Electric-Vehicle Charging Stations? A Perspective on Spatial Heterogeneity and a Non-Linear Relationship J. Sheng et al. 10.3390/su17010086
- A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution D. Ren et al. 10.3390/rs16234492
- Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning H. Wang et al. 10.1016/j.jag.2025.104404
- Investigating the Structural Health of High-Rise Buildings and Its Influencing Factors Using Sentinel-1 Synthetic Aperture Radar Imagery: A Case Study of the Guangzhou–Foshan Metropolitan Area D. Huang et al. 10.3390/buildings14124074
- Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation X. Du et al. 10.1109/ACCESS.2025.3536000
- The impact of urban spatial forms on marine cooling effects in mainland and island regions: A case study of Xiamen, China Y. Shen et al. 10.1016/j.scs.2025.106210
6 citations as recorded by crossref.
- How Does the Urban Built Environment Affect the Accessibility of Public Electric-Vehicle Charging Stations? A Perspective on Spatial Heterogeneity and a Non-Linear Relationship J. Sheng et al. 10.3390/su17010086
- A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution D. Ren et al. 10.3390/rs16234492
- Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning H. Wang et al. 10.1016/j.jag.2025.104404
- Investigating the Structural Health of High-Rise Buildings and Its Influencing Factors Using Sentinel-1 Synthetic Aperture Radar Imagery: A Case Study of the Guangzhou–Foshan Metropolitan Area D. Huang et al. 10.3390/buildings14124074
- Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation X. Du et al. 10.1109/ACCESS.2025.3536000
- The impact of urban spatial forms on marine cooling effects in mainland and island regions: A case study of Xiamen, China Y. Shen et al. 10.1016/j.scs.2025.106210
Latest update: 04 Mar 2025
Short summary
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.
Most existing building height products are limited with respect to either spatial resolution or...
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