Microsoft Maps is releasing country wide open building footprints datasets in United States. This dataset contains 129,591,852 computer generated building footprints derived using our computer vision algorithms on satellite imagery. This data is freely available for download and use.

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

The vintage of the footprints depends on the vintage of the underlying imagery. Bing Imagery is a composite of multiple sources with different capture dates. Each building footprint has a capture date tag associated if we were able to deduce the vintage of imagery source.

Footprints inside the highlighted region on the map are from 2019-2020. There are 73,250,745 such building footprints. This is the focal area where we rerun extraction for the latest release.

The rest of the footprints were extracted from older images, having wider range of capture dates, averaging 2012 year approximately. We have reused footprints from previous releases in this area.

129,591,852 building footprint polygon geometries divided by 50 US states and the District of Columbia in GeoJSON format.

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

GeoJSON is a format for encoding a variety of geographic data structures. For Intensive Documentation and Tutorials, Refer to GeoJson Blog.

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 the aerial image using DNNs
2. Polygonization – Converting building pixel blobs into polygons

The network backbone we used is EfficientNet described here. Although we have millions of labels at our disposal, we found that an effective combination of supervised and unsupervised training yields the best results.

We developed a method that approximates the prediction pixels into polygons making decisions based on the whole prediction feature space. This is very different from standard approaches, e.g. the Douglas-Peucker algorithm, which are greedy in nature. The method tries to impose some of a priori building properties, which is, at the moment, manually defined and automatically tuned. Some of these a priori properties are:

Our metrics show that in the vast majority of cases the quality is at least as good as data hand digitized buildings in OpenStreetMap.

These are the intermediate stage metrics we use to track DNN model improvements and they are pixel based. Pixel recall/precision = 95.5%/94.0%

Match metrics:

We evaluate following metrics to measure the quality of the output:

1. Intersection over Union – This is the standard metric measuring the overlap quality against the labels
2. Shape distance – With this metric we measure the polygon outline similarity
3. Dominant angle rotation error – This measures the polygon rotation deviation

On our evaluation set contains ~15k building. The metrics on the set are:

We estimate <1% false positive ratio in 1000 randomly sampled buildings from the entire output corpus.

EPSG: 4326

They are already available.

The building data are featured in NYTimes article.

A Vector Tile implementation of the data is hosted by Esri.

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