Last update: November, 2025
<aside> 🌍
The Cityweft Global Buildings Dataset Global datasets provides the best coverage of building information across the globe. The dataset combines 25 different data sources covering more than 2.7B building footprints worldwide with the best coverage building heights. They are useful for gaining consistent worldwide context and for applications where wide spatial coverage is more important than absolute precision.
</aside>
Coverage
Worldwide
Detail
Generalized, with some variation depending on source and location
Notes
Values such as building heights may not always be precise and should be reviewed carefully before use in detailed analysis
Updates
Typically maintained and updated on a regular cycle
| Data Source | Coverage | Update Frequency | LOD | Description |
|---|---|---|---|---|
| OpenStreetMaps | Global | Weekly | LOD0-LOD2 | A crowdsourced map database built by volunteers and organizations, covering roads, buildings, and other geographic features around the world. Building heights approximate when available. |
| Microsoft ML Buildings | Global | Periodically | ||
| Last in 2025 | LOD0-LOD1 | Building outlines automatically detected from satellite imagery using Microsoft’s machine learning models. Building heights approximate when available; may include false positive footprints | ||
| Google Open Buildings | Global South | Periodically | ||
| Last in 2023 | LOD0 | A dataset from Google that maps building footprints using AI analysis of satellite images. No building height data; may include false positive footprints. | ||
| East Asian Building Dataset | East Asia | Never | ||
| Produced 2018 | LOD0 | A dataset of building footprints across East Asian countries, created using high-resolution imagery and advanced mapping methods. No building height data. Last update 2023. | ||
| Esri Community Maps | North America | Weekly | LOD0-LOD1 | A dataset of building footprints with height information, contributed by the GIS community and often created or verified by local experts. Reliable building heights. |
| Overture Maps Foundation | Global | Monthly | LOD0-LOD1 | Open, collaboratively maintained geospatial data providing standardized, up-to-date building footprints worldwide. |
<aside> 🏰
Local datasets are provided by regional or municipal data sources and offer greater detail and accuracy within smaller geographic areas. They are well-suited for high-precision analysis and applications that rely on local context.
</aside>
Coverage
City, region, or country-specific
Detail
More accurate building footprints, heights, and geometry compared to global sources
Notes
Best for detailed projects, but coverage is limited to specific locations
| Country | Coverage | Last update | LOD Level | Data source | License | Cityweft Reference Name |
|---|---|---|---|---|---|---|
| Belgium 🇧🇪 | Flanders (Region) | 2015 | LOD1 | Agency for Geographical Information Flanders | Modellicentie voor gratis hergebruik | Municipal Dataset |
| Belgium 🇧🇪 | Wallonie (Region) | 2014 | LOD1 | Service public de Wallonie | CC-BY-4.0 | Municipal Dataset |
| Belgium 🇧🇪 | Brussels (City) | 2012 | LOD1 | Brussels UrbIS | CC-BY-4.0 | Municipal Dataset |
| France 🇫🇷 | Country | 2021 | LOD1 | IGN | License ouverte | Municipal Dataset |
| Germany 🇩🇪 | Berlin (city) | 2014 | LOD1 | Berlin Partner für Wirtschaft und Technologie GmbH | dl-de/by-2-0 | Municipal Dataset |
| Germany 🇩🇪 | Hamburg (City) | 2018 | LOD1 | Landesbetrieb Geoinformation und Vermessung (LGV) | dl-de/by-2-0 | Municipal Dataset |
| Germany 🇩🇪 | Brandenburg (Region) | 2021 | LOD1 | GeoBasis-DE / LGB | dl-de/by-2-0 | Municipal Dataset |
| Germany 🇩🇪 | Freiburg (City) | 2018 | LOD1 | Stadt Freiburg | dl-de/by-2-0 | Municipal Dataset |
| Germany 🇩🇪 | Sachsen (Region) | 2021 | LOD1 | Geobasisinformation und Vermessung Sachsen | dl-de/by-2-0 | Municipal Dataset |
| Germany 🇩🇪 | Sachsen-Anhalt (Region) | 2019 | LOD1 | Landesamt für Digitalisierung, Breitband und Vermessung Bayern | dl-de/by-2-0 | Municipal Dataset |
| Germany 🇩🇪 | Thuerigen (Region) | 2017 | LOD1 | Thüringer Landesamt für Bodenmanagement und Geoinformation | dl-de/by-2-0 | Municipal Dataset |
| Germany 🇩🇪 | Nordrhein Westfalen (Region) | 2021 | LOD1 | Geobasis NRW | dl-de/by-2-0 | Municipal Dataset |
| Germany 🇩🇪 | Niedersachsen (Region) | 2019 | LOD1 | Landesamt für Geoinformation und Landesvermessung Niedersachsen | dl-de/by-2-0 | Municipal Dataset |
| Germany 🇩🇪 | Mecklenburg Vorpommern (Region) | 2019 | LOD1 | Landesamt für innere Verwaltung Mecklenburg Vorpommern | dl-de/by-2-0 | Municipal Dataset |
| Netherlands 🇳🇱 | Country | 2019 | LOD1 | TU Delft 3D | CC-BY-4.0 | Municipal Dataset |
| Poland 🇵🇱 | Country | 2017 | LOD1 | Główny Urząd Geodezji i Kartografii (GUGiK) | CC-BY-3.0 | Municipal Dataset |
| Slovakia 🇸🇰 | Country | 2017 | LOD1 | GKU Bratislava | CC-BY-4.0 | Municipal Dataset |
| Slovenia 🇸🇮 | Country | 2021 | LOD1 | The surveying and mapping authority of the Republic of Slovenia | CC-BY-4.0 | Municipal Dataset |
| Spain 🇪🇸 | Country | 2021 | LOD0 | Instituto Geografico Nacional (IGN) | CC-BY-4.0 | Municipal Dataset |
| USA 🇺🇸 | Country | 2025 | LOD1 | USGS Building Heights | CC0 / Public Domain Dedication | USGS Lidar |
This dataset provides building representations at three Levels of Detail (LOD):