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

2025-06-04

AI Summary

This work addresses the lack of publicly available, globally comprehensive, and high-fidelity open-source 3D building datasets—particularly deficient in spatial coverage, geometric accuracy, and semantic consistency. To overcome these limitations, we propose a quality-driven multi-source building footprint fusion framework and a deep learning–based height inversion method leveraging PlanetScope satellite imagery, enabling joint generation of high-accuracy 2D building polygons and Level-of-Detail 1 (LoD1) 3D models. The resulting Global Building Atlas (GBA) dataset comprises: (i) precise polygonal footprints for 275 million buildings (GBA.Polygon); (ii) a 3-meter-resolution building height map (GBA.Height), 30× finer than prior global products; and (iii) 268 million LoD1 models with >97% height completeness and RMSE ranging from 1.5 to 8.9 m. GBA significantly advances the availability, scalability, and reliability of global 3D building modeling for urban analytics, climate simulation, and geospatial AI.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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 3x3 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 possibilities, 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.

Problem

• Creating a global open dataset of 2D and 3D building data
• Improving accuracy and resolution of global building height maps
• Enabling high-resolution analysis for sustainable development goals

Innovation

• Machine learning pipelines for building data
• Quality-based fusion for enhanced polygons
• Global 3D building models with high resolution