Introduction

Bing Maps is releasing mined roads around the world. We have detected 48.8M 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).

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

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

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