Delineation of Road Networks Using Deep Residual Neural Networks and Iterative Hough Transform

  • Pinjing Xu
  • Charalambos PoullisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11844)


In this paper we present a complete pipeline for extracting road network vector data from satellite RGB orthophotos of urban areas. Firstly, a network based on the SegNeXt architecture with a novel loss function is employed for the semantic segmentation of the roads. Results show that the proposed network produces on average better results than other state-of-the-art semantic segmentation techniques. Secondly, we propose a fast post-processing technique for vectorizing the rasterized segmentation result, removing erroneous lines, and refining the road network. The result is a set of vectors representing the road network. We have extensively tested the proposed pipeline and provide quantitative and qualitative comparisons with other state-of-the-art based on a number of known metrics.


Road network extraction Residual neural networks Semantic segmentation 



This research is based upon work supported by the Natural Sciences and Engineering Research Council of Canada Grants DG-N01670 (Discovery Grant) and DND-N01885 (Collaborative Research and Development with the Department of National Defence Grant).


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Copyright information

© Crown 2019

Authors and Affiliations

  1. 1.Immersive and Creative Technologies Lab, Department of Computer Science and Software Engineering, Gina Cody School of Engineering and Computer ScienceConcordia UniversityMontrealCanada

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