RoadTracer: Automatic Extraction of Road Networks from Aerial Images

Favyen Bastani, Songtao He, Mohammad Alizadeh, Hari Balakrishnan, Samuel Madden, Sanjay Chawla, Sofiane Abbar, David DeWitt
Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, June 2018

Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN. We compare our approach with a segmentation method on fifteen cities, and find that at a 5\% error rate, RoadTracer correctly captures 45% more junctions across these cities.

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Bibtex Entry:

@inproceedings{bastani2018roadtracer,
   author =       "Favyen Bastani and Songtao He and Mohammad Alizadeh and Hari Balakrishnan and Samuel Madden and Sanjay Chawla and Sofiane Abbar and David DeWitt",
   title =        "{RoadTracer: Automatic Extraction of Road Networks from Aerial Images}",
   booktitle =    {Computer Vision and Pattern Recognition (CVPR)},
   year =         {2018},
   month =        {June},
   address =      {Salt Lake City, UT}
}