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.
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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- Development and impact analysis of ground motion datasets for potential strong-to-great seismic scenarios in Chinese mainland H. Zhao et al.
- The Influence Mechanism and Spatial Heterogeneity of Urban Spatial Structure on the Thermal Environment: A Case Study of the Central Urban Area of Jinan J. Wang et al.
- Strategies to accelerate climate risk management in the building sector using data-driven methods and tools M. Charafeddine et al.
- Quantitative assessment of building losses in China’s coastal regions due to sea-level rise under extreme climate conditions H. Zheng et al.
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- Unlocking the potential of urban spatial structure to shape building energy demand at a large scale H. Wang et al.
- Accelerating Computation for Estimating Land Surface Temperature: An Efficient Global–Local Regression (EGLR) Framework J. Liu et al.
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- Complex network analysis of urban heat-cold interaction systems for heat island mitigation: Evidence from Fuzhou, China Z. Shen et al.
- National development pathways shape climate stress at cultural world heritage sites globally H. Cui et al.
- Urbanization and climatic factors drive insect diversity along climatic gradients at urban green spaces in eastern China X. Yao et al.
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- Impacts of multidimensional urban factors on the nighttime light environment and its spatial heterogeneity R. Li et al.
- Spatio-temporal evolution of vertical urban growth in China’s Yangtze River Delta from 1990 to 2020 C. Yin et al.
- Tall and dense urban forms extend growing seasons in Northern cities H. Ma et al.
- GIS-based large-scale urban fire situation spread probability model and simulation technique B. Sun et al.
- The source-sink effects of plain and plateau urban morphology on land surface temperature across seasonal and diurnal cycles: A case study of Lhasa and Wuhan R. Liu et al.
- Global disparities in 3D urban morphology shaped by building height and distributions Y. Che et al.
- GloUCP: a global 1 km spatially continuous urban canopy parameters for the WRF model W. Liao et al.
- Diurnal–Seasonal Contrast of Spatiotemporal Dynamic and the Key Determinants of Surface Urban Heat Islands Across China’s Humid and Arid Regions C. Wang et al.
- High-resolution estimation of urban anthropogenic carbon emissions Z. Lin et al.
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- The multi-scale and multi seasonal effects of 2D/3D morphology on urban thermal environment: Mechanism, importance and thresholds Y. Zhou et al.
- Joint super-resolution and segmentation for 1-m impervious surface area mapping in China’s Yangtze river economic belt J. Deng et al.
- Urban morphology from a landscape perspective: How building morphology distribution land models (BMDLM) emulate pattern and process J. Sturtevant et al.
- Characterizing dynamics of built-up height in China from 2005 to 2020 based on GEDI, Landsat, and PALSAR data P. Chen et al.
- Structure-aware deep learning network for building height estimation Y. Chen et al.
- Mapping city-wide allergenic risk zones of Platanus trees using street view images and deep learning Y. Zhou et al.
- 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.
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- Structural Type Identification of City-Scale Building Groups Based on Deep Learning Z. Xu et al.
- Analysis of wind profile patterns and prediction models in the center of megacities: A case study of Shenzhen F. Li et al.
- Satellite mapping of every building’s function in urban China reveals deep built environment disparities Z. Li et al.
- MFC-CNN: A Deep Learning Framework for Urban Building Height Estimation From GF-7 Imagery D. Cao et al.
- The impact of gray-green space features on thermal environments across urban core-fringe gradients: A path-interaction and non-linear analysis Z. Lin et al.
- Mapping drag coefficient using CFD and machine learning with application to urban canopy model J. Chen et al.
- Spatial and Data-Driven Approaches for Mitigating Urban Heat in Coastal Cities K. Li & H. Wang
- Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning H. Wang et al.
- Integrating TS-InSAR and machine learning for spatiotemporal analysis and influencing factors identification of land subsidence in Nansha, Guangzhou, China Q. He et al.
- Identification of Urban High-Intensity Development Areas Based on Oriented Region Growth-Case Study of Shenzhen City in China J. Qiu et al.
- Modeling service flows in earthquake scenarios using a physical–social multilayer network J. He et al.
- Regulation of water body cooling effects in dense Urban Areas: Roles of spatial configuration, spatial size, and built environment H. Deng et al.
- U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling Y. Cheng et al.
- Vertical 15-minute city: Modeling urban density and functional mix with multi-source geospatial data Y. Chen et al.
- Fine-Scale Mapping of the Wildland–Urban Interface and Seasonal Wildfire Susceptibility Analysis in the High-Altitude Mountainous Areas of Southwestern China S. Li et al.
- Global patterns of inequality in pedestrian shade provision X. Gu et al.
- Food delivery services reduce inequality in urban food accessibility: Evidence from 19 Chinese megacities S. Ye et al.
- Seasonal spatiotemporal variations in intra- and inter-local climate zones using complete urban surface temperature L. Jiang et al.
- Understanding nonlinear and spatially heterogeneous effects of urban residential morphology on land surface temperature: Integrating SOM, XGBoost-SHAP, and GWR models X. Liu et al.
- Estimating Individual Building Heights by Integrating Spaceborne LiDAR and Multisource Remote Sensing Data: A CNN–Transformer Model and a Semi-Supervised Sample Augmentation Approach S. Du et al.
- Community-level education percentile rank estimation in China using multi-source big data and machine learning Y. Zhang et al.
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Saved (final revised paper)
Latest update: 08 May 2026
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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