CsBoundary: City-Scale Road-Boundary Detection in Aerial Images for High-Definition Maps

Zhenhua Xu, Yuxuan Liu, Lu Gan, Xiangcheng Hu, Yuxiang Sun, Ming Liu, Lujia Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one important information presented in HD maps since it distinguishes between road areas and off-road areas, which can guide vehicles to drive within road areas. But it is labor-intensive to annotate road boundaries for HD maps at the city scale. To enable automatic HD map annotation, current work uses semantic segmentation or iterative graph growing for road-boundary detection. However, the former could not ensure topological correctness since it works at the pixel level, while the latter suffers from inefficiency and drifting issues. To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation. Our network takes as input an aerial image patch, and directly infers the continuous road-boundary graph (i.e., vertices and edges) from this image. To generate the city-scale road-boundary graph, we stitch the obtained graphs from all the image patches. Our csBoundary is evaluated and compared on a public benchmark dataset. The results demonstrate our superiority. The project page is available at https://sites.google.com/view/csboundary/

Original languageEnglish
Pages (from-to)5063-5070
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - 1 Apr 2022


  • Autonomous driving
  • city-scale road-boundary detection
  • HD map
  • self-attention

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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