RoadDetections

Introduction

Bing Maps is releasing mined roads around the world. We have detected 54.2M km of roads worldwide. Mining is performed with Bing Maps imagery including Maxar and Airbus. The data is freely available for download and use under the Open Data Commons Open Database License (ODbL). We plan to opensource both NN model and geometry generation code in first half of 2025.

Data

		Region
		Length in '000 Km
		File size in MB
    



	

		Australia and Oceania
		2314.7383
	

	

		Caribbean
		243.776
	

	

		Central America
		1538.3427
	

	

		Central Asia
		1204309
	

	

		Eastern Africa
		1668.8360
	

	

		Eastern Asia
		153.948
	

	

		Eastern Europe
		4601.41382
	

	

		Middle Africa
		513.8112
	

	

		Northern Africa
		1387.2388
	

	

		Northern America
		12990.63865
	

	

		Northern Europe
		2380985
	

	

		South America
		5694.71245
	

	

		Southeastern Asia
		2777680
	

	

		Southern Africa
		1217.9241
	

	

		Southern Asia
		5676.31467
	

	

		Southern Europe
		2727.7972
	

	

		Western Africa
		1130.3306
	

	

		Western Asia
		2444.4756
	

	

		Western Europe
		3560.51410
	

	

		World
		54225.216564

FAQ

What is the data format?

Each file has all roads from a certain geographical region. Each row in a file has an Alpha-3 code and a geojson of a road (Alpha-3 code of a region where the road geojson approximately is) separated with TAB (\t). Each geojson also contains property "WidthMeters" - approximate width of the road in meters.

World is divided into subregions for better usability based on United Nations geoscheme

Alpha-3 codes are used from IBAN and Wikipedia page. Also refer to AlphaCodeToRegionName.tsv file (some smaller regions/disputed areas might have ambigious codes)

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

Data generation details:

The road extraction is done in two major stages:

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

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. However data is up-to-date with freshest available imagery from Microsoft Maps.

How good is the data?

The result of the pipeline (after going through conflation, cutting, filtering and quality control reached 95% precision and pushed into Microsoft Maps production.

Why is the data being released?

Microsoft has a continued interest in supporting a thriving OpenStreetMap (OSM) ecosystem.

Next steps

We will opensource both NN model and geometry generation code in 2025

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.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Legal Notices

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