Efficient Sparse Street Furniture Extraction from Mobile Laser Scanning Point Clouds

L. Truong-Hong*, R. C. Lindenbergh, M. J. Vermeij

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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Current mobile systems are capable of efficiently acquiring dense urban point clouds. Still, operational use of such data is hampered by the lack of efficient object extraction methodology. Notably methodology is lacking for automatically extracting objects that do not belong to the road furniture like street signs and light poles but do belong to the street furniture. As an example, object we consider public garbage bins, that are installed and should be maintained in public areas in every city. However, information about types, locations, and condition of these public garbage bins are rarely updated and only obtained through manual measurements. Therefore, an efficient way of collecting information on such public objects is of interest not only for urban management but also when developing digital twins of a city. This study proposes a new method to automatically extract public garbage bins from large urban mobile laser scanning (MLS) point clouds. The proposed method consists of three main steps: (1) cell-, (2) sub-cell-, and (3) surface-based filtering, in which both spatial information of the point clouds and contextual knowledge of the public garbage bins are incorporated to efficiently remove irrelevant 3D points at an early phase and identify and classify different types of public garbage bins. Contextual knowledge includes shape and dimensions, and the relationship between the public garbage bins and the ground surface. A MLS dataset of the city centre of Rotterdam, the Netherlands, consisting of 2.84 billion points organised in 166 tiles of 50 × 75m, and covering an area of about 750 × 750m was used to test the proposed method. Results show that the method can automatically extract ∼90 public garbage bins with an overall detection rate of 89.1%. Moreover, the executing time for the entire dataset was only about 163.6 minutes, which is equivalent to 3.46 seconds per one million points. Although the method was tested here one public garbage bins, it can be easily tuned for the detection of other street furniture objects, like benches, post boxes or bollards.

Original languageEnglish
Pages (from-to)161-168
Number of pages8
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue number4/W4-2022
Publication statusPublished - 2022
Event17th 3D GeoInfo Conference, 3DGeoinfo 2022 - Sydney, Australia
Duration: 19 Oct 202221 Oct 2022


  • Cells
  • Kernel Density
  • object identification
  • Point Cloud
  • Sub-cell
  • Surface Extraction
  • Voxel
  • Voxel-Based Region Growing


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