Google Open Buildings analysis workflow: building footprints, confidence filtering, and spatial analysis

What Google Open Buildings contains

The primary Open Buildings V3 product is a large collection of building detections derived from high-resolution satellite imagery. Google describes the release as containing approximately 1.8 billion building detections over an inference area of about 58 million square kilometers. Coverage includes parts of Africa, South Asia, Southeast Asia, Latin America, and the Caribbean; it is not worldwide coverage.

Each polygon record contains the latitude and longitude of the footprint centroid, the polygon area in square meters, a model confidence score, the building geometry in WKT POLYGON or MULTIPOLYGON form, and a full Plus Code corresponding to the centroid. The dataset does not include building type, street address, ownership, occupancy, construction date, or other descriptive details. The points product provides point representations, while the polygon product preserves the inferred footprint geometry. (sites.research.google)

• •V3 polygons: individual inferred building footprints with confidence and area attributes.
• •V3 points: centroid-style point records for building detections.
• •2.5D Temporal: annual raster estimates of presence, fractional counts, and height for 2016–2023.
• •Plus Codes: included for polygon centroids, but not a substitute for a formal address registry.

Geography, temporal coverage, and update cadence

The V3 polygon inference was carried out in May 2023 using high-resolution satellite imagery. That date describes the inference campaign, not a uniform observation date for every footprint. Google explains that source-image freshness varies by location: some areas may have relatively recent imagery, while other areas may rely on imagery several years old or may not have been processed. Sensitive areas, including some conflict zones, were omitted, and the covered area is not necessarily complete even within a listed country. (sites.research.google)

There is no published fixed update cadence for the V3 polygon dataset. Google states that it hopes to refresh the data and extend coverage, but users should not assume that a particular country, tile, or building has a guaranteed refresh schedule. Version history records V1, V2, and V3 inference releases; previous versions remain available through corresponding cloud paths according to the project documentation.

• •V3 polygon inference campaign: May 2023.
• •V3 freshness: varies by location and source imagery; no fixed update cadence is stated.
• •2.5D Temporal coverage: annual observations from 2016–2023.
• •Geographic omissions and sensitive-area exclusions mean that absence from the dataset is not proof that no buildings exist.

Formats, access methods, and a defensible analysis workflow

The V3 polygon and point data are distributed as spatially sharded CSV files, with one file per S2 level-4 cell. Polygon files can be very large—up to approximately 7.8 GB per file—and the full polygon collection is approximately 178 GB. Point files total approximately 48 GB, with individual files reported up to about 2.1 GB. Polygon rows contain WKT geometry, so a practical workflow should use a streaming or columnar ingestion process rather than loading the entire collection into a desktop GIS at once. (sites.research.google)

Google provides a map-based download option, cloud-storage paths for all polygon or point shards, metadata describing tile geometry and URLs, and a score-threshold CSV. A Colab notebook is available for downloading data for a country or region. The temporal product is available through Google Earth Engine as an Image Collection and can also be downloaded for a selected area and timeframe through Google’s documented workflow. (sites.research.google)

• •Select relevant S2 level-4 shards before downloading or parsing.
• •Use the score-threshold metadata to choose a location-specific precision target.
• •Keep low-confidence and excluded records available for sensitivity analysis rather than silently deleting them.
• •Validate geometries and inspect a sample against imagery or trusted local building data.
• •Aggregate only after confirming the coordinate system, tile boundaries, and duplicate-handling strategy.
• •Record version, inference period, source imagery context where available, threshold choice, and retrieval date.

Suitable GIS workflows and interpretation cautions

Open Buildings is well suited to regional building-density mapping, settlement delineation, disaster-exposure screening, population-estimation inputs, health-facility catchment studies, and environmental-pressure analysis. For example, an analyst can intersect confidence-filtered footprints with flood zones to estimate the number and area of detected structures inside a hazard area, or join building counts to administrative polygons to compare settlement density across districts. A spatial join to roads, schools, clinics, or transit stops can support proximity and accessibility studies, provided that the other layers have appropriate positional accuracy and dates.

The dataset can also support change-oriented work when combined with other time-stamped evidence. The 2.5D Temporal product is particularly appropriate for comparing annual built-up presence or estimated height across large regions. However, annual raster values should not be interpreted as a precise inventory of individual construction events. A change in a pixel may reflect model uncertainty, image conditions, building morphology, or aggregation effects as well as actual construction or demolition.

• •Good for regional or population-scale analysis, not guaranteed parcel-level legal or engineering decisions.
• •Use imagery review and local reference data for high-consequence applications.
• •Do not infer occupancy, building use, address validity, or ownership from footprint geometry.
• •Treat missing coverage, low confidence, and imagery-age variation as analytical conditions, not incidental metadata.

License, strengths, and tradeoffs

Google states that Open Buildings is available under both the Creative Commons Attribution 4.0 license and the Open Data Commons Open Database License 1.0. Users may choose which license to use, but must comply with the terms of the selected license and the project’s published disclaimer. The choice can matter when combining the data with other open geographic datasets, redistributing derived databases, or incorporating the results into commercial or public-facing products. Legal review may be appropriate for a production workflow. (sites.research.google)

• •Strength: exceptional scale for building-footprint analysis across regions where authoritative or complete local building data may be difficult to obtain.
• •Strength: explicit confidence scores and location-specific precision thresholds make quality filtering more transparent than an unqualified binary footprint layer.
• •Strength: multiple access patterns—CSV shards, cloud storage, map exploration, Earth Engine, and a separate annual raster product—support both data-engineering and remote-sensing workflows.
• •Tradeoff: coverage is geographically limited and includes omissions, including sensitive areas and unprocessed regions.
• •Tradeoff: freshness is uneven for V3 polygons, and no fixed update cadence is published.
• •Tradeoff: detections are model outputs with false positives, false negatives, geometry errors, and difficult cases such as dense or high-rise construction.
• •Tradeoff: the source files are extremely large, and WKT-in-CSV is less convenient for cloud-native spatial analysis than a directly queryable columnar format.
• •Tradeoff: the data contains footprints and related model attributes, not authoritative addresses, building classifications, occupancy, parcel boundaries, or legal building records.