Metrics for aerial, urban lidar point clouds

Michael H. Stanley, Debra F. Laefer

Research output: Contribution to journalArticlepeer-review

Abstract

This paper introduces five new density and accuracy metrics for aerial point clouds that address the complexity and objectives of modern, dense laser scans of urban scenes. The five metrics describe (1) vertical surface density (points per area on vertical surfaces); (2) vertical density as a function of horizontal density; (3) vertical surface accuracy; and a decomposition of error into (4) within-pass and (5) cross-pass components. Specifically considered is vertical surface coverage and the practice of overlapping flight passes to reduce the occlusions and achieve the vertical density needed for twenty-first-century use cases (e.g. curb and window detection). The application of these metrics to a quartet of recent urban flyovers demonstrates their relevance by establishing (1) the efficacy of considering sensor position and wall height when predicting point density on vertical surfaces; (2) that cross-pass registration accounts for a disproportionate amount of the vertical surface error (but not horizontal) and provides a meaningful parameter to compare high-density, urban point clouds; and (3) that compared to horizontal density and accuracy, the vertical counterparts are disproportionately impacted (positively for density and negatively for accuracy) by modern, optimized flight missions.

Original languageEnglish (US)
Pages (from-to)268-281
Number of pages14
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume175
DOIs
StatePublished - May 2021

Keywords

  • LiDAR
  • LiDAR accuracy
  • LiDAR density
  • Remote sensing
  • registration error
  • urban aerial laser scanning

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences

Fingerprint

Dive into the research topics of 'Metrics for aerial, urban lidar point clouds'. Together they form a unique fingerprint.

Cite this