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Abstract

This paper presents new aerial image analysis algorithms that, from highway ortho-images, produce lane-level detailed maps. We analyze screenshots of road vectors to obtain the relevant spatial and photometric cues of road image-regions. We then refine the obtained patterns to generate hypotheses about the true road-lanes. A road-lane hypothesis, since it explains only a part of the true road-lane, is then linked to other hypotheses to completely delineate boundaries of the true road-lanes. Finally, some of the refined image cues about the underlying road network are used to guide a linking process of road-lane hypotheses. We tested the accuracy and robustness of our algorithms with high-resolution, inter-city highway ortho-images. Experimental results show promise in producing lane-level detailed highway maps from ortho-image analysis – 89% of the true road-lane boundary pixels were successfully detected and 337 out of 417 true road-lanes were correctly recovered.

Keywords

lane-level highway map extraction, ortho image anaysis, computer vision, machine learning

Notes

Sponsor: General Motors

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): Enhanced Road Network Data from Overhead Imagery

Text Reference

Young-Woo Seo, Christopher Urmson, and David Wettergreen, "Ortho-Image Analysis for Producing Lane-Level Highway Maps," International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2012), November, 2012, pp. 506-509.

BibTeX Reference

@inproceedings{Seo_2012_7325,
   author = "Young-Woo Seo and Christopher Urmson and David Wettergreen",
   title = "Ortho-Image Analysis for Producing Lane-Level Highway Maps",
   booktitle = "International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2012)",
   pages = "506-509",
   publisher = "ACM",
   month = "November",
   year = "2012",
}

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