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Point Density Variations in Airborne Lidar Point Clouds.

Vaclav Petras1, Anna Petrasova1, James B McCarter2

  • 1Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Dr., Campus Box 7106, Raleigh, NC 27695, USA.

Sensors (Basel, Switzerland)
|February 11, 2023
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Summary
This summary is machine-generated.

Airborne lidar point clouds show density variations that can cause errors. This study examines these variations, their sources, and methods like decimations and homogenizations to reduce them for better topographic and geospatial modeling.

Keywords:
airborne lidargeospatial mappinglaser scanningnonuniform point distributionpoint density patternremote sensingsurface topography

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Area of Science:

  • Geospatial Science
  • Remote Sensing
  • Geomatics

Background:

  • Airborne lidar point clouds are crucial for topographic and geospatial modeling.
  • Increasing point density and accuracy in lidar data do not eliminate point density variations.
  • These variations can indicate data quality issues and lead to errors in derived products.

Purpose of the Study:

  • To highlight and analyze point density variations in airborne lidar datasets.
  • To identify the sources and implications of these density variations.
  • To discuss methods for reducing point density variations and their suitability.

Main Methods:

  • Overview of point density variations in lidar data.
  • Examination of six airborne lidar point cloud datasets.
  • Literature review to identify sources and issues related to density variations.
  • Discussion of reduction techniques including decimations and homogenizations.

Main Results:

  • Point density variations are common in airborne lidar datasets, despite advancements.
  • These variations can stem from various sources and negatively impact derived geospatial products.
  • Decimation and homogenization techniques show potential for mitigating density variations.

Conclusions:

  • Understanding and addressing point density variations in airborne lidar is essential for accurate topographic and geospatial modeling.
  • Identifying the sources of these variations aids in data quality assessment.
  • Decimations and homogenizations offer viable strategies for improving lidar data consistency.