RoadTracer: Automatic Extraction of Road Networks From Aerial Images

Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, David DeWitt; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4720-4728

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.

Related Material


[pdf] [Supp] [arXiv]
[bibtex]
@InProceedings{Bastani_2018_CVPR,
author = {Bastani, Favyen and He, Songtao and Abbar, Sofiane and Alizadeh, Mohammad and Balakrishnan, Hari and Chawla, Sanjay and Madden, Sam and DeWitt, David},
title = {RoadTracer: Automatic Extraction of Road Networks From Aerial Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}