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Bing Maps is releasing open building footprints around the world. We have detected 777M buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.

This data is licensed by Microsoft under the Open Data Commons Open Database License (ODbL).

776,712,641 building footprint polygon geometries located around the world in line delimited GeoJSON format.

GeoJSON is a format for encoding a variety of geographic data structures. For intensive documentation and tutorials, refer to this blog.

Microsoft has a continued interest in supporting a thriving OpenStreetMap ecosystem.

Maybe. Never overwrite the hard work of other contributors or blindly import data into OSM without first checking the local quality. While our metrics show that this data meets or exceeds the quality of hand-drawn building footprints, the data does vary in quality from place to place, between rural and urban, mountains and plains, and so on. Inspect quality locally and discuss an import plan with the community. Always follow the OSM import community guidelines.

Yes. Currently Microsoft Open Buildings dataset is used in ml-enabler for task creation. You can try it out at AI assisted Tasking Manager. The data will also be made available in Facebook RapiD.

The building extraction is done in two stages:

1. Semantic Segmentation – Recognizing building pixels on an aerial image using deep neural networks (DNNs)
2. Polygonization – Converting building pixel detections into polygons

We did not apply any modeling improvements for this release. Instead, we focused on scaling our approach to increase coverage, and trained models regionally.

The evaluation metrics are computed on a set of building polygon labels for each region. Note, we only have verification results for Mexico buildings since we did not train a model for the country.

Building match metrics on the evaluation set:

We track the following metrics to measure the quality of matched building polygons in the evaluation set:

1. Intersection over Union – This is a standard metric measuring the overlap quality against the labels
2. Dominant angle rotation error – This measures the polygon rotation deviation

False positives are estimated per country from randomly sampled building polygon predictions.

Vintage of extracted building footprints depends on the vintage of the underlying imagery. The underlying imagery is from Bing Maps including Maxar and Airbus between 2014 and 2021.

Our metrics show that in the vast majority of cases the quality is at least as good as hand digitized buildings in OpenStreetMap. It is not perfect, particularly in dense urban areas but it provides good recall in rural areas.

EPSG: 4326

Maybe. This is a work in progress. Also, check out our other building releases!

• US
• Australia
• Canada
• Uganda and Tanzania
• South America
• Kenya and Nigeria
• Indonesia, Malaysia, and the Philippines

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