# GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Models arXiv: https://arxiv.org/abs/2506.04106 ยท HTML: https://mlsanity.com/paper/2506.04106/ ## 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 (most cited first, 11 of 11) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014) - [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022) - [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016) - [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018) - [High-Resolution Building and Road Detection from Sentinel-2](https://mlsanity.com/paper/2310.11622/) (2023) - [Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands](https://mlsanity.com/paper/2201.01191/) (2021) - [HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images](https://mlsanity.com/paper/2309.16486/) (2023) - [Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing](https://mlsanity.com/paper/2505.01385/) (2025) - [GlobalBuildingMap -- Unveiling the Mystery of Global Buildings](https://mlsanity.com/paper/2404.13911/) (2024) ## Built on by (most cited first, 16 of 16) - [TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery](https://mlsanity.com/paper/2511.12104/) (2025) - [TSE-Net: Semi-supervised Monocular Height Estimation from Single Remote Sensing Images](https://mlsanity.com/paper/2511.13552/) (2025) - [Smart Transfer: Leveraging Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagery](https://mlsanity.com/paper/2604.02627/) (2026) - [Global Building Area Estimation Products: How Accurate Are They?](https://mlsanity.com/paper/2607.19766/) (2026) - [Hot H'em: S`ai G`on Gi~ua C'ai N'ong Hong C`ong B`ang -- Saigon in Unequal Heat](https://mlsanity.com/paper/2512.11896/) (2025) - [Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications](https://mlsanity.com/paper/2601.06056/) (2025) - [GeoFormer: A Lightweight Swin Transformer for Joint Building Height and Footprint Estimation from Sentinel Imagery](https://mlsanity.com/paper/2602.09932/) (2026) - [LoD-Loc v3: Generalized Aerial Localization in Dense Cities using Instance Silhouette Alignment](https://mlsanity.com/paper/2603.19609/) (2026) - [The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation](https://mlsanity.com/paper/2603.29245/) (2026) - [Test-Time Adaptation for Height Completion via Self-Supervised ViT Features and Monocular Foundation Models](https://mlsanity.com/paper/2604.02009/) (2026) - [Morphology-Guided Cross-Task Coupling for Joint Building Height and Footprint Estimation](https://mlsanity.com/paper/2605.04731/) (2026) - [Mapping the City Through the Lens of Language Models](https://mlsanity.com/paper/2608.02971/) (2026) - [ArchEGraph: A Large-Scale Graph Dataset for Geometry-Topology-Physics Aligned Building Energy Modeling](https://mlsanity.com/paper/2608.06772/) (2026) - [UHI-Bench: Benchmarking Dual-Source Urban Heat Island Modeling Across Cities in Diverse Climate Regimes](https://mlsanity.com/paper/2608.23857/) (2026) - [A comparative assessment of global building and settlement datasets across geographic and settlement contexts](https://mlsanity.com/paper/2609.28154/) (2026) - [UniBuild: Unified Building Mapping From Multi-Source Optical Remote Sensing Imagery With Detail Decoding and Geometry Regularization](https://mlsanity.com/paper/2609.37031/) (2026) ## 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.