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Fully Automated Algorithm for Light Pole Detection and Mapping in Rural Highway Environment Using Mobile Light

Maged Gouda1, Amr Shalkamy1, Xinyi Li2

  • 1Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada.

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Summary
This summary is machine-generated.

This study introduces an automated method for mapping light poles using LiDAR point-cloud data on rural highways. The approach accurately identifies light pole locations, improving road safety audits and asset inventory.

Keywords:
LiDAR dataasset managementbig data analyticsdata analyticsdata and data scienceinformation systems and technologyinfrastructurelight poles mappinglow-volume roadspoint cloud dataremote sensingroadside safety designroadway designsafety

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Area of Science:

  • Geospatial technology
  • Transportation engineering
  • Road safety

Background:

  • Light detection and ranging (LiDAR) data is increasingly used for roadside asset management.
  • Mapping light poles on rural highways is challenging due to limited research and safety concerns related to their placement.
  • Traditional manual data collection for light pole assets is time-consuming and labor-intensive.

Purpose of the Study:

  • To develop and validate an automated approach for mapping light pole locations using LiDAR point-cloud data.
  • To address the gap in research concerning light pole mapping on rural highways.
  • To facilitate automated light pole inventory and enhance road safety assessments.

Main Methods:

  • Extraction and smoothing of scanning vehicle trajectory to segment LiDAR point-cloud data.
  • Application of filters to extract pole-like objects and density-based clustering for point grouping.
  • Utilizing a geometric filter for the final extraction of light poles.

Main Results:

  • The automated algorithm was tested on 28 km of rural highway data in Alberta, Canada.
  • The proposed method achieved high accuracy, with average precision, recall, and F1 scores exceeding 98%.
  • The results demonstrate the effectiveness of the automated approach compared to previous studies.

Conclusions:

  • The developed automated method accurately maps light pole locations from LiDAR data.
  • This approach can significantly improve the efficiency of light pole inventory and road safety audits for transportation agencies.
  • The study contributes to the advancement of automated roadside asset management and safety analysis.