Awesome
Introduction
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.
Regions included
License
This data is licensed by Microsoft under the Open Data Commons Open Database License (ODbL).
FAQ
What does the data include?
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 |
What is the GeoJSON format?
GeoJSON is a format for encoding a variety of geographic data structures. For intensive documentation and tutorials, refer to GeoJson blog.
Why is the data being released?
Microsoft has a continued interest in supporting a thriving OpenStreetMap ecosystem.
Should we import the data into OpenStreetMap?
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.
Will the data be used or made available in larger OpenStreetMap ecosystem?
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.
How did we create the data?
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
Stage1: Semantic Segmentation
Stage 2: Polygonization
Were there any modeling improvements used for this release?
We did not apply any modeling improvements for this release.
Evaluation set metrics
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 positive ratio in the corpus
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% |
What is the vintage of this data?
Vintage of extracted building footprints depends on vintage of the underlying imagery. Underlying imagery is from Maxar between 2016 and 2020.
How good is the data?
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.
What is the coordinate reference system?
EPSG: 4326
Will there be more data coming for other geographies?
Maybe. This is a work in progress. Also, check out our other building releases!
<br>Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
Legal Notices
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