Preprints
https://doi.org/10.5194/essd-2025-327
https://doi.org/10.5194/essd-2025-327
08 Jul 2025
 | 08 Jul 2025
Status: this preprint is currently under review for the journal ESSD.

GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Models

Xiao Xiang Zhu, Sining Chen, Fahong Zhang, Yilei Shi, and Yuanyuan Wang

Abstract. We introduce GlobalBuildingAtlas, a publicly available dataset providing global and complete coverage of building polygons, heights and Level of Detail 1 (LoD1) 3D building models. This is the first open dataset to offer high quality, consistent, and complete building data in 2D and 3D form at the individual building level on a global scale. Towards this dataset, we developed machine learning-based pipelines to derive building polygons and heights (called GBA.Height) from global PlanetScope satellite data, respectively. Also a quality-based fusion strategy was employed to generate higher-quality polygons (called GBA.Polygon) based on existing open building polygons, including our own derived one. With more than 2.75 billion buildings worldwide, GBA.Polygon surpasses the most comprehensive database to date by more than 1 billion buildings. GBA.Height offers the most detailed and accurate global 3D building height maps to date, achieving a spatial resolution of 3×3 meters—30 times finer than previous global products (90 m), enabling a high-resolution and reliable analysis of building volumes at both local and global scales. Finally, we generated a global LoD1 building model (called GBA.LoD1) from the resulting GBA.Polygon and GBA.Height. GBA.LoD1 represents the first complete global LoD1 building models, including 2.68 billion building instances with predicted heights, i.e., with a height completeness of more than 97 %, achieving RMSEs ranging from 1.5 m to 8.9 m across different continents. With its height accuracy, comprehensive global coverage and rich spatial details, GlobalBuildingAltas offers novel insights on the status quo of global buildings, which unlocks unprecedented geospatial analysis possiblities, as showcased by a better illustration of where people live and a more comprehensive monitoring of the progress on the 11th Sustainable Development Goal of the United Nations. The code is publicly available at https://github.com/zhu-xlab/GlobalBuildingAtlas.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Xiao Xiang Zhu, Sining Chen, Fahong Zhang, Yilei Shi, and Yuanyuan Wang

Status: open (until 14 Aug 2025)

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Xiao Xiang Zhu, Sining Chen, Fahong Zhang, Yilei Shi, and Yuanyuan Wang

Model code and software

GlobalBuildingAtlas Xiao Xiang Zhu, Sining Chen, Fahong Zhang, Yilei Shi, Yuanyuan Wang https://github.com/zhu-xlab/GlobalBuildingAtlas

Xiao Xiang Zhu, Sining Chen, Fahong Zhang, Yilei Shi, and Yuanyuan Wang

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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.
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