RoadTracer: Automatic Extraction of Road Networks from Aerial Images
Name
roadtracer-cvpr18.pdf
Description
Accepted version
Size
4.27 MB
Format
Adobe PDF
Checksum (MD5)
63901292b7a29786f4061ebfd81a35f3
Author(s)
Bastani, Favyen
He, Songtao
Abbar, Sofiane
Alizadeh Attar, Mohammadreza
Balakrishnan, Hari
Chawla, Sanjay
Madden, Samuel R
DeWitt, David J
Date Issued
December 18, 2018
Journal
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR.2018.00496
