Ross ROSS = Recommend OSS · open-source software intelligence for agents

Microsoft Building Footprints resource

Worldwide building footprints derived from satellite imagery observed · 2026-08-28

github.com/microsoft/GlobalMLBuildingFootprints · Python · NOASSERTION (other) observed · 2026-08-28

Health v2 · maintenance only

76/100

  • Activity 97
  • Release rhythm 35
  • Longevity 100

Flags: no_releases no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 1594
  • days_rel: n/a
  • days_push: 19
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1940 stars · 276 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Open datasets of computer-generated building footprint polygons for the United States (and globally) derived by Microsoft using deep learning on satellite imagery. The US dataset contains over 129 million footprints in GeoJSON format, released under the ODbL license to support the OpenStreetMap ecosystem.

Use cases

  • download building footprints for all US states as GeoJSON
  • import computer-generated building outlines into OpenStreetMap
  • analyze building density and coverage for urban planning research
  • use building polygons as training or evaluation data for computer vision models
  • enrich GIS applications with nationwide building geometry data
  • support humanitarian mapping with AI-assisted tasking

When to choose

  • you need large-scale, freely licensed building footprint polygons for the US or globally
  • you want GeoJSON data ready for GIS tools or OSM workflows
  • you need building geometry with associated capture-date metadata

When to avoid

  • you need hand-verified or survey-grade building boundaries
  • you require frequently refreshed, near-real-time footprint data
  • you need data under a permissive license without ODbL share-alike obligations

Facets

dataset · maturity active

machine-learning computer-vision geospatial data-science computer-vision artificial-intelligence cross-platform building-footprints satellite-imagery geo open-data openstreetmap semantic-segmentation microsoft maps geospatial

1 source

Member repositories

For agents

markdown · JSON · MCP: product_card(name="microsoft/GlobalMLBuildingFootprints")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem