mlsanityData to 14 Oct 2026

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

arXiv 2506.04106 ↗First posted 4 Jun 2025cs.CVcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 11 of its 59 references that are in the map. First posted on arXiv 4 Jun 2025.

Builds on

11 of 59 references.

References · 11

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  4. 2022A ConvNet for the 2020scited by 3,579
  5. 2016Pyramid Scene Parsing Networkcited by 2,479
  6. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,271
  7. 2023High-Resolution Building and Road Detection from Sentinel-2cited by 15
  8. 2021Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlandscited by 14
  9. 2023HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Imagescited by 8
  10. 2025Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensingcited by 4
  11. 2024GlobalBuildingMap -- Unveiling the Mystery of Global Buildingscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 12.

16 papers.

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  1. 2025TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagerycited by 3
  2. 2025TSE-Net: Semi-supervised Monocular Height Estimation from Single Remote Sensing Imagescited by 1
  3. 2026Smart Transfer: Leveraging Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagerycited by 1
  4. 2026Global Building Area Estimation Products: How Accurate Are They?cited by 1
  5. 2025Hot H'em: S`ai G`on Gi~ua C'ai N'ong Hong C`ong B`ang -- Saigon in Unequal Heatcited by 0
  6. 2025Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implicationscited by 0
  7. 2026GeoFormer: A Lightweight Swin Transformer for Joint Building Height and Footprint Estimation from Sentinel Imagerycited by 0
  8. 2026LoD-Loc v3: Generalized Aerial Localization in Dense Cities using Instance Silhouette Alignmentcited by 0
  9. 2026The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimationcited by 0
  10. 2026Test-Time Adaptation for Height Completion via Self-Supervised ViT Features and Monocular Foundation Modelscited by 0
  11. 2026Morphology-Guided Cross-Task Coupling for Joint Building Height and Footprint Estimationcited by 0
  12. 2026Mapping the City Through the Lens of Language Modelscited by 0
4 more
  1. 2026ArchEGraph: A Large-Scale Graph Dataset for Geometry-Topology-Physics Aligned Building Energy Modelingcited by 0
  2. 2026UHI-Bench: Benchmarking Dual-Source Urban Heat Island Modeling Across Cities in Diverse Climate Regimescited by 0
  3. 2026A comparative assessment of global building and settlement datasets across geographic and settlement contextscited by 0
  4. 2026UniBuild: Unified Building Mapping From Multi-Source Optical Remote Sensing Imagery With Detail Decoding and Geometry Regularizationcited by 0