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

Related authors

GloUCP: A global 1 km spatially continuous urban canopy parameters for the WRF model
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-408,https://doi.org/10.5194/essd-2024-408, 2024
Revised manuscript accepted for ESSD
Short summary
U-Surf: A Global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
Yifan Cheng, Lei Zhao, Tirthankar Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-416,https://doi.org/10.5194/essd-2024-416, 2024
Revised manuscript accepted for ESSD
Short summary

Related subject area

Domain: ESSD – Land | Subject: Land Cover and Land Use
Revised and updated geospatial monitoring of 21st century forest carbon fluxes
David A. Gibbs, Melissa Rose, Giacomo Grassi, Joana Melo, Simone Rossi, Viola Heinrich, and Nancy L. Harris
Earth Syst. Sci. Data, 17, 1217–1243, https://doi.org/10.5194/essd-17-1217-2025,https://doi.org/10.5194/essd-17-1217-2025, 2025
Short summary
ChatEarthNet: a global-scale image–text dataset empowering vision–language geo-foundation models
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 17, 1245–1263, https://doi.org/10.5194/essd-17-1245-2025,https://doi.org/10.5194/essd-17-1245-2025, 2025
Short summary
Aboveground biomass dataset from SMOS L-band vegetation optical depth and reference maps
Simon Boitard, Arnaud Mialon, Stéphane Mermoz, Nemesio J. Rodríguez-Fernández, Philippe Richaume, Julio César Salazar-Neira, Stéphane Tarot, and Yann H. Kerr
Earth Syst. Sci. Data, 17, 1101–1119, https://doi.org/10.5194/essd-17-1101-2025,https://doi.org/10.5194/essd-17-1101-2025, 2025
Short summary
GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep learning methods
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data, 17, 855–880, https://doi.org/10.5194/essd-17-855-2025,https://doi.org/10.5194/essd-17-855-2025, 2025
Short summary
Annual vegetation maps in the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data, 17, 773–797, https://doi.org/10.5194/essd-17-773-2025,https://doi.org/10.5194/essd-17-773-2025, 2025
Short summary

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. 
Download
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.
Share
Altmetrics
Final-revised paper
Preprint