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https://github.com/microsoft/RoadDetections
Road detections from Microsoft Maps aerial imagery
https://github.com/microsoft/RoadDetections
Last synced: 2 months ago
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Road detections from Microsoft Maps aerial imagery
- Host: GitHub
- URL: https://github.com/microsoft/RoadDetections
- Owner: microsoft
- License: other
- Created: 2021-04-23T20:31:09.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-15T17:25:16.000Z (almost 2 years ago)
- Last Synced: 2024-05-18T18:34:19.260Z (8 months ago)
- Size: 5.79 MB
- Stars: 495
- Watchers: 18
- Forks: 24
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
- awesome-open-transport - (dataset) Road Detections from Microsoft
README
Introduction
-------------------
Bing Maps is releasing mined roads around the world. We have detected 48.9M km of all roads and 1165K km of roads missing from OSM. Mining is performed with Bing Maps imagery between 2020 and 2022 including Maxar and Airbus. The data is freely available for download and use under the [Open Data Commons Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/).
## Data
Date
All ML derived Roads
ML derived Roads missing from OSM
Region Length in '000 Km
Region Length in '000 Km
20 May 2020
United States
9,308
United States
818
21 Mar 2021
South America
4,480
South America
98
21 Jan 2022
Caribbean Islands
232
Caribbean Islands
5
03 Mar 2022
Middle East
3,444
Middle East
84
05 Apr 2022
Central Asia
1,204
Central Asia
28
18 Apr 2022
Northern Africa
1,077
Northern Africa
24
28 Apr 2022
Western Africa
982
Western Africa
32
28 Apr 2022
Central Africa
324
Central Africa
6
12 May 2022
Eastern Africa
1,151
Eastern Africa
31
12 May 2022
Southern Africa
1,506
Southern Africa
40
08 Jun 2022
Europe
10,212
N/A
N/A
03 Jul 2022
Oceania
1,947
N/A
N/A
27 Jul 2022
Central America
1,376
N/A
N/A
03 Aug 2022
Canada
1,832
N/A
N/A
13 Aug 2022
South Asia
3,723
N/A
N/A
12 Sep 2022
Southeastern Asia
2,744
N/A
N/A
19 Sep 2022
North Asia
2,259
N/A
N/A
27 Feb 2023
Japan
1,105
N/A
N/A
## FAQ
#### 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](http://geojson.org/)
#### Data generation details:
The road extraction is done in four stages (full drop went through two stages and OSM missing set went through all four):
1. Semantic Segmentation – Recognizing road pixels on the aerial image using Convolutional Neural Network (CNN).
2. Geometry Generation - A series of algorithms and processes transforming output of semantic segmentation into roads in geometry format.
- Image postprocessing
- Thinning
- Connectivity improvement
- Graph construction
- Finalizing road shapes and network quality
- Stiching road geojsons between neighboring images where needed
3. Conflation & Cutting - Excluding roads and parts of roads that already exist in the road network (OSM).
4. Classification - A classifier to filter out low-confidence roads and predict a road type.
#### Neural network architecture and dataset
Our network was based on UNet and ResNet and the following papers [U-Net] (https://arxiv.org/abs/1505.04597), [Res U-Net] (https://arxiv.org/pdf/1512.03385.pdf), [Res U-Net] (https://arxiv.org/pdf/1711.10684.pdf).
The model was trained on 512x512 images, it is fully-convolutional, which allows images of any size (that is divisable by 64) be processed by the model (constrained by GPU memory, 1088x1088 in our case). The training set consists of 20000 labeled images. Majority of the satellite images cover diverse areas all around the world. To achieve a good set representation, we have enriched the set with samples from various areas covering mountains, glaciers, forests, deserts, beaches, coasts, etc.
Images in the set are of 1088x1088 pixel size with 100 cm/pixel resolution. The training is done with Keras toolkit.
#### Metrics
We measure intermediate stage metrics to track performance of our models. Pixel metric measures performance of the the Convolutional Neural Network and APLS metric (Average Path Length Similarity) measures overall connectivity after geometry generation stage.
| Metric | Precision | Recall |
| ------------- |:-------------:|:-------------:|
|Pixel|85.24%|82.81%|
|APLS|87.53%|79.33%|
#### Data Vintage
The vintage of the roads depends on the vintage of the underlying imagery. Because Bing Imagery is a composite of multiple sources it is difficult to know the exact dates for individual pieces of data.
#### How good is the data?
The Osm Missing Data went through a final classifier to ensure that the precision is at least 95% (90% for USA now - to be updated to 95% in 2022). After classifier filters out potentially bad roads we remeasure the precision and make sure that it is 95% before releasing results
#### Why is the data being released?
Microsoft has a continued interest in supporting a thriving OpenStreetMap ecosystem.
# 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.opensource.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., status check, 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](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
# Legal Notices
Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation
may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries.
The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks.
Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653.
Privacy information can be found at https://privacy.microsoft.com/en-us/
Microsoft and any contributors reserve all other rights, whether under their respective copyrights, patents,
or trademarks, whether by implication, estoppel or otherwise.