Bing Maps is releasing open building footprints in the Philippines, Indonesia, and Malaysia. We have detected 88,653,552 buildings from 2016-2020 Maxar imagery. The data is freely available for download and use under applicable license.
This data is licensed by Microsoft under the Open Data Commons Open Database License (ODbL).
88,653,552 building footprint polygon geometries located in Malaysia, Indonesia, and the Philippines in line-delimited GeoJSON format. You may download the data in GeoJSON format here:
| Location | Building Count | Link | Size (Compressed) |
|---|---|---|---|
| Indonesia | 63,947,880 | Indonesia.geojsonl.zip | 4.4GB |
| Philippines | 17,421,764 | Philippines.geojsonl.zip | 1.1GB |
| Malaysia | 7,283,908 | Malaysia.geojsonl.zip | 548MB |
GeoJSON is a format for encoding a variety of geographic data structures. For intensive documentation and tutorials, refer to GeoJson 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:
- Semantic Segmentation – Recognizing building pixels on an aerial image using deep neural networks (DNNs)
- Polygonization – Converting building pixel detections into polygons
We did not apply any modeling improvements for this release.
The evaluation metrics are computed on a set of 6,000 building polygon labels across the three countries.
Building match metrics on the evaluation set:
| Countries | Precision | Recall |
|---|---|---|
| PH + ID + MY | 88.64% | 77.53% |
We track the following metrics to measure the quality of matched building polygons in the evaluation set:
- Intersection over Union – This is a standard metric measuring the overlap quality against the labels
- Dominant angle rotation error – This measures the polygon rotation deviation
| Countries | IoU | Rotation error [deg] |
|---|---|---|
| PH + ID + MY | 65.49% | 6.57 |
False positives are estimated per country from 18,851 randomly sampled building polygon predictions.
| Country | Buildings Sampled | False Positives |
|---|---|---|
| Philippines | 9,870 | 1.77% |
| Indonesia | 4,987 | 2.98% |
| Malaysia | 4994 | 1.84% |
Vintage of extracted building footprints depends on vintage of the underlying imagery. Underlying imagery is from Maxar between 2016 and 2020.
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!
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