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

• URL: http://arxiv.org/abs/2506.04106v1
• Date: Wed, 04 Jun 2025 15:59:32 GMT
• Title: GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Models
• Authors: Xiao Xiang Zhu, Sining Chen, Fahong Zhang, Yilei Shi, Yuanyuan Wang,
• Abstract summary: This is the first open dataset to offer high quality, consistent, and complete building data in 2D and 3D form on a global scale.<n>With more than 2.75 billion buildings worldwide, GBA.Polygon surpasses the most comprehensive database to date by more than 1 billion buildings.<n>With its height accuracy, comprehensive global coverage and rich spatial details, GlobalBuildingAltas offers novel insights on the status quo of global buildings.
• Score: 16.945349721168068
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset [90.97440987655084]
  Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources.<n>To address these challenges, we introduce the first comprehensive multimodal Urban Digital Twin benchmark dataset: TUM2TWIN.<n>This dataset includes georeferenced, semantically aligned 3D models and networks along with various terrestrial, mobile, aerial, and satellite observations boasting 32 data subsets over roughly 100,000 $m2$ and currently 767 GB of data.
  arXiv  Detail & Related papers  (2025-05-12T09:48:32Z)
• CULTURE3D: A Large-Scale and Diverse Dataset of Cultural Landmarks and Terrains for Gaussian-Based Scene Rendering [12.299096433876676]
  Current state-of-the-art 3D reconstruction models face limitations in building extra-large scale outdoor scenes.<n>In this paper, we present a extra-large fine-grained dataset with 10 billion points composed of 41,006 drone-captured high-resolution aerial images.<n>Compared to existing datasets, ours offers significantly larger scale and higher detail, uniquely suited for fine-grained 3D applications.
  arXiv  Detail & Related papers  (2025-01-12T20:36:39Z)
• Segmentation-aware Prior Assisted Joint Global Information Aggregated 3D Building Reconstruction [6.839442579589125]
  Multi-View Stereo plays a pivotal role in civil engineering by facilitating 3D modeling, precise engineering surveying, quantitative analysis, as well as monitoring and maintenance. However, Multi-View Stereo algorithms encounter challenges in reconstructing weakly-textured regions within large-scale building scenes. In these areas, the stereo matching of pixels often fails, leading to inaccurate depth estimations. We propose an algorithm that accurately segments weakly-textured regions and constructs their plane priors. This function selects optimal plane prior information based on global information in the prior candidate set, constrained by geometric consistency during the depth estimation update process.
  arXiv  Detail & Related papers  (2024-10-24T04:59:44Z)
• CMAB: A First National-Scale Multi-Attribute Building Dataset in China Derived from Open Source Data and GeoAI [1.3586572110652484]
  This paper presents the first national-scale Multi-Attribute Building dataset (CMAB) covering 3,667 spatial cities, 29 million buildings, and 21.3 billion square meters of rooftops. Using billions of high-resolution Google Earth images and 60 million street view images (SVIs), we generated rooftop, height, function, age, and quality attributes for each building. Our dataset and results are crucial for global SDGs and urban planning.
  arXiv  Detail & Related papers  (2024-08-12T02:09:25Z)
• MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
  This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan.<n>The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-06-13T17:59:30Z)
• A global product of fine-scale urban building height based on spaceborne lidar [14.651500878252723]
  We provide an up-to-date global product of urban building heights based on a fine grid size of 150 m around 2020. The estimated method of building height samples based on the GEDI data was effective with 0.78 of Pearson's r and 3.67 m of RMSE. This work will boost future urban studies across many fields including climate, environmental, ecological, and social sciences.
  arXiv  Detail & Related papers  (2023-10-22T16:51:15Z)
• Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds [4.38301148531795]
  Existing datasets for 3D modeling mainly focus on common objects such as furniture or cars. We present a urban-scale dataset consisting of more than 160 thousands buildings along with corresponding point clouds, mesh and wire-frame models, covering 16 cities in Estonia about 998 Km2. Experimental results indicate that Building3D has challenges of high intra-class variance, data imbalance and large-scale noises.
  arXiv  Detail & Related papers  (2023-07-21T21:38:57Z)
• Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation [59.6553058160943]
  We propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OpenStreetMap data. The proposed method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data.
  arXiv  Detail & Related papers  (2023-07-05T18:16:30Z)
• UniG3D: A Unified 3D Object Generation Dataset [75.49544172927749]
  UniG3D is a unified 3D object generation dataset constructed by employing a universal data transformation pipeline on ShapeNet datasets. This pipeline converts each raw 3D model into comprehensive multi-modal data representation. The selection of data sources for our dataset is based on their scale and quality.
  arXiv  Detail & Related papers  (2023-06-19T07:03:45Z)
• Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
  We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery. Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
  arXiv  Detail & Related papers  (2023-01-04T05:19:33Z)
• Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges [52.624157840253204]
  We present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points. Our dataset consists of large areas from three UK cities, covering about 7.6 km2 of the city landscape. We evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results.
  arXiv  Detail & Related papers  (2020-09-07T14:47:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.