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Updated: May 5, 2026

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LiDAR-Based Road Surface Damage Classification: A Survey.

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PubMed
Summary
This summary is machine-generated.

LiDAR technology offers superior road surface damage classification by directly measuring geometry, unlike cameras limited by lighting. This review details LiDAR

Keywords:
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Area of Science:

  • Geospatial Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Image-only road surface damage detection systems struggle with variable lighting conditions (shadows, glare, low contrast).
  • LiDAR (Light Detection and Ranging) technology directly captures surface geometry, enabling precise, depth-aware measurements.
  • Existing methods lack a comprehensive overview of LiDAR's application across the entire road damage classification pipeline.

Purpose of the Study:

  • To survey and analyze LiDAR-based road surface damage classification techniques.
  • To cover the full pipeline from data acquisition to multisensor fusion and practical deployment.
  • To bridge the gap between LiDAR measurements and practical pavement management practices.

Main Methods:

  • Review of LiDAR acquisition methods (mobile and terrestrial laser scanning).
  • Analysis of preprocessing, data representation, and machine learning techniques (supervised, semi-supervised, unsupervised).
  • Examination of multisensor fusion strategies (early, mid, late stages) and evaluation protocols.

Main Results:

  • LiDAR enables direct measurement and quantification of road surface damage, moving beyond simple detection.
  • Classification methods are mapped to established agency practices like the Distress Identification Manual and Pavement Condition Index.
  • Datasets and evaluation protocols for detection, segmentation, 3D reconstruction, and ride quality are summarized.

Conclusions:

  • LiDAR-based road surface damage classification is a robust and scalable solution.
  • Practical deployment requires addressing calibration, intensity normalization, tiling, and runtime.
  • Future research should focus on severity-aware and highly scalable field systems.