Sat2Graph: Road Graph Extraction Through Graph-Tensor Encoding
Name
2007.09547.pdf
Description
Accepted version
Size
3.62 MB
Format
Adobe PDF
Checksum (MD5)
11e63fe12eb8e08c4921b2f035266bd4
Author(s)
He, S
Bastani, F
Jagwani, S
Alizadeh, M
Balakrishnan, H
Chawla, S
Elshrif, MM
Madden, S
Sadeghi, MA
Date Issued
2020
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer International Publishing
Citation
He, S, Bastani, F, Jagwani, S, Alizadeh, M, Balakrishnan, H et al. 2020. "Sat2Graph: Road Graph Extraction Through Graph-Tensor Encoding." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12369 LNCS.
Version
Author's final manuscript
Abstract
© 2020, Springer Nature Switzerland AG. Inferring road graphs from satellite imagery is a challenging computer vision task. Prior solutions fall into two categories: (1) pixel-wise segmentation-based approaches, which predict whether each pixel is on a road, and (2) graph-based approaches, which predict the road graph iteratively. We find that these two approaches have complementary strengths while suffering from their own inherent limitations. In this paper, we propose a new method, Sat2Graph, which combines the advantages of the two prior categories into a unified framework. The key idea in Sat2Graph is a novel encoding scheme, graph-tensor encoding (GTE), which encodes the road graph into a tensor representation. GTE makes it possible to train a simple, non-recurrent, supervised model to predict a rich set of features that capture the graph structure directly from an image. We evaluate Sat2Graph using two large datasets. We find that Sat2Graph surpasses prior methods on two widely used metrics, TOPO and APLS. Furthermore, whereas prior work only infers planar road graphs, our approach is capable of inferring stacked roads (e.g., overpasses), and does so robustly.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-030-58586-0_4
