RoadTracer: Automatic Extraction of Road Networks from Aerial Images
Author(s)Bastani, Favyen; He, Songtao; Abbar, Sofiane; Alizadeh Attar, Mohammadreza; Balakrishnan, Hari; Chawla, Sanjay; Madden, Samuel R; DeWitt, David J; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Institute of Electrical and Electronics Engineers (IEEE)
Bastani, Favyen, et al. “RoadTracer: Automatic Extraction of Road Networks from Aerial Images.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18-23 June 2018, Salt Lake City, Utah, USA, IEEE, 2018, pp. 4720–28.
Author's final manuscript