Robust approach for urban road surface extraction using mobile laser scanning 3D point clouds

A. Nurunnabi*, F. N. Teferle, R. C. Lindenbergh, J. Li, S. Zlatanova

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity. Many methods are available for road pavement, curb and roadside way extraction. Most of them use classical approaches that do not mitigate problems caused by the presence of noise and outliers. In practice, however, laser scanning point clouds are not free from noise and outliers, and it is apparent that the presence of a very small portion of outliers and noise can produce unreliable and non-robust results. A road surface usually consists of three key parts: road pavement, curb and roadside way. This paper investigates the problem of road surface extraction in the presence of noise and outliers, and proposes a robust algorithm for road pavement, curb, road divider/islands, and roadside way extraction using MLS point clouds. The proposed algorithm employs robust statistical approaches to remove the consequences of the presence of noise and outliers. It consists of five sequential steps for road ground and non-ground surface separation, and road related components determination. Demonstration on two different MLS data sets shows that the new algorithm is efficient for road surface extraction and for classifying road pavement, curb, road divider/island and roadside way. The success can be rated in one experiment in this paper, where we extract curb points; the results achieve 97.28%, 100% and 0.986 of precision, recall and Matthews correlation coefficient, respectively.

Original languageEnglish
Pages (from-to)59-66
Number of pages8
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue numberB1-2022
Publication statusPublished - 2022
Event2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission I - Nice, France
Duration: 6 Jun 202211 Jun 2022


  • Autonomous Driving
  • Curb
  • Filtering
  • Intelligent Transportation
  • Mobile Mapping
  • Road Safety
  • Robust Regression


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