RoadTagger: Robust Road AttributeInference with Graph Neural Networks

Songtao He, Favyen Bastani, Satvat Jagwani, Edward Park, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Samuel Madden, Mohammad Amin Sadeghi
The Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, NY, February 2020

Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused by a fundamental limitation the limited effective receptive field of image classifiers. To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. Using a GNN allows information to propagate on the road network graph and eliminates the receptive field limitation of image classifiers. We evaluate RoadTagger on both a large real-world dataset covering 688 km^2 area in 20 U.S. cities and a synthesized dataset. In the evaluation, RoadTagger improves inference accuracy over the CNN image classifier based approaches. In addition, RoadTagger is robust to disruptions in the satellite imagery and is able to learn complicated inductive rules for aggregating scattered information along the road network.

[PDF (0KB)]

Bibtex Entry:

   author =       "Songtao He and Favyen Bastani and Satvat Jagwani and Edward Park and Sofiane Abbar and Mohammad Alizadeh and Hari Balakrishnan and Sanjay Chawla and Samuel Madden and Mohammad Amin Sadeghi",
   title =        "{RoadTagger: Robust Road AttributeInference with Graph Neural Networks}",
   booktitle =    {The Thirty-Fourth AAAI Conference on Artificial Intelligence},
   year =         {2020},
   month =        {February},
   address =      {New York, NY}