RoadTracer: Automatic Extraction of Road Networks from Aerial Images

• Main Authors: Bastani, Favyen (Author), He, Songtao (Author), Abbar, Sofiane (Author), Alizadeh Attar, Mohammadreza (Author), Balakrishnan, Hari (Author), Chawla, Sanjay (Author), Madden, Samuel R (Author), DeWitt, David J (Author)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE), 2019-06-10T19:20:06Z.
• Subjects: Article
• Online Access: Get fulltext

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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