Paper Info
Title
Unthule: An Incremental Graph Construction Process for Robust Road Map Extraction from Aerial Images.
Abstract
The availability of highly accurate maps has become crucial due to the increasing importance of location-based mobile applications as well as autonomous vehicles. However, mapping roads is currently an expensive and human-intensive process. 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. show that these segmentation methods have high error rates (poor precision) because noisy CNN outputs are difficult to correct. We propose a novel approach, Unthule, to construct highly accurate road maps from aerial images. In contrast to prior work, Unthule uses an incremental search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN. train the CNN to output the direction of roads traversing a supplied point in the aerial imagery, and then use this CNN to incrementally construct the graph. compare our approach with a segmentation method on fifteen cities, and find that Unthule has a 45% lower error rate in identifying junctions across these cities.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Pattern recognition,Convolutional neural network,Segmentation,Computer science,Word error rate,Road map,Incremental search,Heuristics,Artificial intelligence,Pixel,Connectivity
DocType
Volume
Citations 
Journal
abs/1802.03680
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Favyen Bastani1959.78
Han-gen He28712.70
Sofiane Abbar314117.23
Mohammad Alizadeh4148277.16
Hari Balakrishnan5316653441.21
Sanjay Chawla61372105.09
David J. DeWitt7129433559.25
Samuel Madden8161011176.38