Articles | Volume 17, issue 12
https://doi.org/10.5194/essd-17-6647-2025
© Author(s) 2025. 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-17-6647-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
01 Dec 2025
Data description article |
| 01 Dec 2025
| 01 Dec 2025
GlobalBuildingAtlas: an open global and complete dataset of building polygons, heights and LoD1 3D models
Technical University of Munich, Munich, Germany
Munich Center for Machine Learning, Munich, Germany
Sining Chen
Technical University of Munich, Munich, Germany
Munich Center for Machine Learning, Munich, Germany
Fahong Zhang
Technical University of Munich, Munich, Germany
Yilei Shi
Technical University of Munich, Munich, Germany
Yuanyuan Wang
Technical University of Munich, Munich, Germany
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Total article views: 4,241 (including HTML, PDF, and XML)
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Total article views: 86,888 (including HTML, PDF, and XML)
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Total article views: 82,647 (including HTML, PDF, and XML)
Thereof 82,631 with geography defined
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Total article views: 4,241 (including HTML, PDF, and XML)
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32 citations as recorded by crossref.
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- Development and validation of the CanHS: The Canada housing stock datasets for buildings and postal codes L. Minaker et al. https://doi.org/10.1016/j.ubtr.2026.100048
- High Spatial Resolution Building Characteristics for the Global South: Insights from the Google Open Buildings Temporal Dataset (2016-2023) R. Priyatikanto et al. https://doi.org/10.12688/gatesopenres.16386.1
- Satellite earth observation for recovery from hydrometeorological hazards H. Friedrich et al. https://doi.org/10.1016/j.wasec.2026.100205
- SSDFNet: A State Space Guided Network for Multimodal Building Extraction and Solar Modeling Y. Tan et al. https://doi.org/10.1109/JSTARS.2026.3689074
- Global Flood Vulnerability Model: Building-Level Assessment Using Multi-Source Remote Sensing S. Olagunju et al. https://doi.org/10.3390/rs18091425
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- SCS-Net: A Semantic-Contour Synergy Network for Precise Building Delineation From High-Resolution Remote Sensing Imagery T. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3687175
- Multi-Scale LiDAR Analysis of Urban Vegetation, Built Morphology, and Population Density in Zagreb L. Rumora et al. https://doi.org/10.3390/rs18111715
- Machine learning for urban wind simulation: A comprehensive review I. Karadag et al. https://doi.org/10.1016/j.scs.2026.107614
- Technical to deployable potential of rooftop solar photovoltaics M. Shi et al. https://doi.org/10.1038/s44359-026-00179-w
- A high-resolution framework for urban pluvial flood risk mapping A. Vogelbacher et al. https://doi.org/10.5194/nhess-26-2765-2026
- A survival guide for assessing global fire risks to natural and human systems C. Steinmann et al. https://doi.org/10.1016/j.ijdrr.2026.106140
- Groundwater and geothermal archetypes in Berlin, Germany M. Hajizadeh Javaran et al. https://doi.org/10.1186/s40517-026-00375-8
- Enhancing Urban and Peri-Urban Zoning Using Spatially Constrained Clustering: Evidence from the Jakarta–Bandung Mega-Urban Region N. Rahma et al. https://doi.org/10.3390/land15040534
- Modelling spatio-temporal distribution of urban population - A high-resolution model for German cities P. Priesmeier et al. https://doi.org/10.1016/j.compenvurbsys.2026.102419
- Tiny-Dataset Scale-Consistent Diffusion Framework for Single SAR Image Building Height Estimation Q. Li et al. https://doi.org/10.1109/TGRS.2026.3693803
- Road and Building Reconstruction from 3D LiDAR Point Clouds: A Scoping Review N. Krishnakumar et al. https://doi.org/10.1007/s11831-026-10580-0
- Terrain-Aware Optical-SAR Image Registration Under Severe Geometric Distortions Z. Bai et al. https://doi.org/10.1109/JSTARS.2026.3671068
- Identifying local climate zones in two U.S. Midwestern cities: a comparison of methods D. Habeeb et al. https://doi.org/10.1007/s43762-026-00261-w
- Mapping parcel-level Essential Urban Land Use Categories across global cities (EULUC-Globe) B. Chen et al. https://doi.org/10.1016/j.scib.2026.06.053
- SAR-based individual-building damage identification and large-scale earthquake damage prediction H. Liu et al. https://doi.org/10.1016/j.jag.2026.105260
- Graph-driven urban structure analysis: Typology inference and performance linkage from VHR imagery Y. Guo et al. https://doi.org/10.1016/j.scs.2026.107527
- Enabling Circular Reuse of Sandwich Panels Through UAV Inspection, Deep Learning, and BIM-Based Material Passports R. Garcia et al. https://doi.org/10.3390/su18052454
- A transferable and interpretable approach to slum mapping using building morphometrics and optical imagery H. Yang et al. https://doi.org/10.1080/15481603.2026.2649308
- LUCIDiT: A Lean Urban Comfort Intelligent Digital Twin for Quick Mean Radiant Temperature Assessment M. Baia et al. https://doi.org/10.3390/atmos17030305
- Refined 3D Urban Building Reconstruction from TomoSAR Point Clouds via Multi-Level Geometric Priors and Shadow Analysis W. Liu et al. https://doi.org/10.3390/s26134028
- Evaluating the suitability of the GlobalBuildingAtlas for digital urban infrastructure analysis in India G. Dandabathula et al. https://doi.org/10.1088/2634-4505/ae7f49
- Spatial Patterns of Energy-Related Carbon Emissions from Residential Land: A Hybrid Physics–Machine-Learning Study of Shenzhen L. Yao et al. https://doi.org/10.3390/land15050772
- Enhancing Monocular Building Height Estimation via Weak Supervision From Imperfect Labels S. Chen et al. https://doi.org/10.1109/JSTARS.2026.3688751
- Towards a Comparison of the Semantic Information of Pan-European Open Building Data L. Gabrielli et al. https://doi.org/10.3390/ijgi15060252
- Multihazard Disaster Damage Classification for Buildings in Optical Satellite Images M. Zhang & Z. Chen https://doi.org/10.1061/AOMJAH.AOENG-0077
32 citations as recorded by crossref.
- Giant 3D map shows almost every building in the world M. Basu https://doi.org/10.1038/d41586-025-04036-x
- Development and validation of the CanHS: The Canada housing stock datasets for buildings and postal codes L. Minaker et al. https://doi.org/10.1016/j.ubtr.2026.100048
- High Spatial Resolution Building Characteristics for the Global South: Insights from the Google Open Buildings Temporal Dataset (2016-2023) R. Priyatikanto et al. https://doi.org/10.12688/gatesopenres.16386.1
- Satellite earth observation for recovery from hydrometeorological hazards H. Friedrich et al. https://doi.org/10.1016/j.wasec.2026.100205
- SSDFNet: A State Space Guided Network for Multimodal Building Extraction and Solar Modeling Y. Tan et al. https://doi.org/10.1109/JSTARS.2026.3689074
- Global Flood Vulnerability Model: Building-Level Assessment Using Multi-Source Remote Sensing S. Olagunju et al. https://doi.org/10.3390/rs18091425
- Land cover classification of Sentinel-1 images at subpixel level using deep learning U. Habiba et al. https://doi.org/10.1080/2150704X.2026.2661869
- SCS-Net: A Semantic-Contour Synergy Network for Precise Building Delineation From High-Resolution Remote Sensing Imagery T. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3687175
- Multi-Scale LiDAR Analysis of Urban Vegetation, Built Morphology, and Population Density in Zagreb L. Rumora et al. https://doi.org/10.3390/rs18111715
- Machine learning for urban wind simulation: A comprehensive review I. Karadag et al. https://doi.org/10.1016/j.scs.2026.107614
- Technical to deployable potential of rooftop solar photovoltaics M. Shi et al. https://doi.org/10.1038/s44359-026-00179-w
- A high-resolution framework for urban pluvial flood risk mapping A. Vogelbacher et al. https://doi.org/10.5194/nhess-26-2765-2026
- A survival guide for assessing global fire risks to natural and human systems C. Steinmann et al. https://doi.org/10.1016/j.ijdrr.2026.106140
- Groundwater and geothermal archetypes in Berlin, Germany M. Hajizadeh Javaran et al. https://doi.org/10.1186/s40517-026-00375-8
- Enhancing Urban and Peri-Urban Zoning Using Spatially Constrained Clustering: Evidence from the Jakarta–Bandung Mega-Urban Region N. Rahma et al. https://doi.org/10.3390/land15040534
- Modelling spatio-temporal distribution of urban population - A high-resolution model for German cities P. Priesmeier et al. https://doi.org/10.1016/j.compenvurbsys.2026.102419
- Tiny-Dataset Scale-Consistent Diffusion Framework for Single SAR Image Building Height Estimation Q. Li et al. https://doi.org/10.1109/TGRS.2026.3693803
- Road and Building Reconstruction from 3D LiDAR Point Clouds: A Scoping Review N. Krishnakumar et al. https://doi.org/10.1007/s11831-026-10580-0
- Terrain-Aware Optical-SAR Image Registration Under Severe Geometric Distortions Z. Bai et al. https://doi.org/10.1109/JSTARS.2026.3671068
- Identifying local climate zones in two U.S. Midwestern cities: a comparison of methods D. Habeeb et al. https://doi.org/10.1007/s43762-026-00261-w
- Mapping parcel-level Essential Urban Land Use Categories across global cities (EULUC-Globe) B. Chen et al. https://doi.org/10.1016/j.scib.2026.06.053
- SAR-based individual-building damage identification and large-scale earthquake damage prediction H. Liu et al. https://doi.org/10.1016/j.jag.2026.105260
- Graph-driven urban structure analysis: Typology inference and performance linkage from VHR imagery Y. Guo et al. https://doi.org/10.1016/j.scs.2026.107527
- Enabling Circular Reuse of Sandwich Panels Through UAV Inspection, Deep Learning, and BIM-Based Material Passports R. Garcia et al. https://doi.org/10.3390/su18052454
- A transferable and interpretable approach to slum mapping using building morphometrics and optical imagery H. Yang et al. https://doi.org/10.1080/15481603.2026.2649308
- LUCIDiT: A Lean Urban Comfort Intelligent Digital Twin for Quick Mean Radiant Temperature Assessment M. Baia et al. https://doi.org/10.3390/atmos17030305
- Refined 3D Urban Building Reconstruction from TomoSAR Point Clouds via Multi-Level Geometric Priors and Shadow Analysis W. Liu et al. https://doi.org/10.3390/s26134028
- Evaluating the suitability of the GlobalBuildingAtlas for digital urban infrastructure analysis in India G. Dandabathula et al. https://doi.org/10.1088/2634-4505/ae7f49
- Spatial Patterns of Energy-Related Carbon Emissions from Residential Land: A Hybrid Physics–Machine-Learning Study of Shenzhen L. Yao et al. https://doi.org/10.3390/land15050772
- Enhancing Monocular Building Height Estimation via Weak Supervision From Imperfect Labels S. Chen et al. https://doi.org/10.1109/JSTARS.2026.3688751
- Towards a Comparison of the Semantic Information of Pan-European Open Building Data L. Gabrielli et al. https://doi.org/10.3390/ijgi15060252
- Multihazard Disaster Damage Classification for Buildings in Optical Satellite Images M. Zhang & Z. Chen https://doi.org/10.1061/AOMJAH.AOENG-0077
Saved (final revised paper)
Latest update: 20 Jul 2026
Short summary
We introduce GlobalBuildingAtlas, a publicly available dataset offering global and complete coverage of building polygons (GBA.Polygon), heights (GBA.Height) and Level of Detail 1 3D models (GBA.LoD1). This is the first open dataset to offer high quality, consistent, and complete building data in 2D and 3D at the individual building level on a global scale. With more than 2.75 billion buildings worldwide, it surpasses the most comprehensive database to date by more than 1 billion buildings.
We introduce GlobalBuildingAtlas, a publicly available dataset offering global and complete...
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