Mapping roads through deep learning and weakly supervised training
Creating accurate maps today is a painstaking, time-consuming manual process, even with access to satellite imagery and mapping software. Many regions — particularly in the developing world — remain largely unmapped. To help close this gap, Facebook AI researchers and engineers have developed a new method that uses deep learning and weakly supervised training to predict road networks from commercially available high-resolution satellite imagery. The resulting model sets a new bar for the state of the art for accuracy, and because it is able to accommodate regional differences in road networks, it can effectively predict roads around the globe.
We are now sharing the details of our model and making data available to the global mapping community through , a new set of specialized map-editing services and tools. Map With AI includes an editor interface, , which allows mapping experts to easily review, verify, and adjust the map as needed.
We used this system to map all the previously unmapped roads in Thailand — more than 300,000 miles’ worth — in (OSM), a community-based effort to create freely available, editable maps of the world. We were able to complete this project in 18 months — less than half the time it would have taken a team of 100 mapping experts to do it manually.
Accurate mapping data helps us better serve people everywhere with products such as and . Map With AI also aligns with our core goals: to connect people and ensure everyone is represented on the map. As with Facebook AI’s project, these maps will also be publicly available as a resource for disaster response, urban planning, development projects, and many other use cases. When floods hit Kerala, India, in 2018, for example, Map With AI expedited mapping of the region by the Humanitarian OpenStreetMap Team (HOT) to assist in relief efforts. We hope that RapiD will accelerate OSM and HOT volunteers’ work to create freely available maps of regions around the world.
We’ve pushed our mapping research forward on several fronts. At CVPR 2018, we helped organize the , advancing the state of the art in satellite image analysis by providing data sets and a competition platform to host and evaluate computer vision and machine learning solutions. We are also developing new and suited to the problem space of remote sensing; investigating weakly supervised learning techniques to apply our road mapping work at a global scale; and working with our mapping team to test these approaches at scale and build the right tooling.
In extracting roads from satellite imagery, we’ve leveraged recent advances in using fully convolutional neural networks for semantic segmentation in conjunction with large-scale weakly supervised learning. Road detection is a straightforward application of semantic segmentation where the road is the foreground and the rest of the image is the background. As shown in the graphic below, the output of this process is a rasterized map showing how confidently the model can predict whether each pixel of the input satellite imagery is a road. For our road segmentation, we’ve used a modified version of the architecture that won the DeepGlobe Satellite Road Extraction Challenge. Vectorization and postprocessing techniques can then take these outputs and convert them into road vectors compatible with geospatial databases such as OSM.
As part of our Thailand road-mapping project, we had human experts review and correct the road networks that the AI system identified. We then used these manually corrected maps as training data for the model. The Thailand project mapped the country’s entire road network, so we could be confident of the accuracy and completeness of the data. We found that training on this data set produced highly accurate validation results for Thailand, but accuracy dropped sharply for other regions.
