We have developed a new process for automatic road network extraction from satellite imagery. If you need the most up-to-date view of road networks, our method is a significant improvement on established processes.
Key takeaways:
What would it mean for your organization if you could automatically extract road networks from a satellite image? If, at the click of a button, you could generate up-to-date maps showing the location of all kinds of roads and their connections at junctions?
Automatic road network extraction from satellite imagery has long been a major ambition for the mapping industry. In June 2024, the team behind BrightEarth published a paper describing our new method that uses deep learning for road detection.
We believe this is a significant step forward for satellite imagery road mapping. Learn more about the process, and how it benefits you.
When they are mapping out roads, most organizations that use advanced GIS applications today rely on open-source inputs. Most often, they use solutions like OpenStreetMap (OSM). OSM is updated by volunteers who identify road networks (among many other features) and their location.
As valuable as these sources are, they have certain drawbacks:
The team behind BrightEarth has developed a new method that uses deep learning for road detection. Rather than relying on open-source data when you create maps, our technique uses the latest satellite image of a place of interest as the input. We then apply an AI process we developed that automatically identifies all roads from the image, and extracts entire networks as a vector for use in a GIS map.
The three-step process is as follows:
We have developed a neural network that identifies road interior and road contour. When part of the road isn’t visible (e.g. because a tree or building occludes it), the system is trained to smooth loss.
After roads are segmented, our process reconstructs the network and turns this into a vector. The process cleans clutter and ‘noise’ from the network, creates a skeleton of the network, and identifies the optimal location of connections between roads through graph optimization..
A final stage in our satellite imagery road mapping process is to identify the road surface. Our process classifies roads into either processed (i.e. ‘man-made’ materials like concrete, tarmac etc.) or unprocessed (i.e. sand, dirt, gravel, etc.). This assessment is also informed by our Land Use Land Cover layer - if the surrounding landscape is mainly sand, then we estimate an unprocessed road is also made of sand.
Once this process is complete, a vector layer is made available to download for use in advanced GIS applications.
To verify the accuracy of our new process for satellite imagery road mapping, we conducted three experiments in cities around the world with very different kinds of road networks: Timbuktu, Amman, and Aden.
We selected satellite images of areas in the three cities, and then our human experts carried out a manual road classification process, identifying all roads in the image.
Then, we applied our automatic road network extraction process. When compared to the manual approach, our results were very encouraging:
Image: Road networks generated by our pipeline over Timbuktu, Amman, and Aden (top to bottom rows), with input satellite images, road segmentation, our extracted road networks, the Ground Truth, and crops (left to right columns). In the crops, Ground Truths are marked in red and our results are in yellow.
What this data shows is that automatic road network extraction from satellite imagery can be almost as accurate as human extraction, while also being fast and reliable.
We believe our new satellite imagery road mapping process can make a real difference for a wide range of sectors.
We believe our new method for automatic road network extraction from satellite imagery is a significant step forward. It will help multiple organizations that want to develop road networks, improve planning, and offer users better infrastructure. And we’re continually working to further enhance the process, with improved processes for complex scenarios (such as elevated bridges and intertwined elevated roads).
Interested in trying our AI-powered, satellite imagery road-mapping solution? Join BrightEarth’s Early Adopter Program, and experience it for yourself.