Develop Guidelines for Integration of UAS LiDAR and Photogrammetry to Enhance Land Surveying Capabilities

Unmanned aircraft systems (UASs) equipped with digital cameras, light detection and ranging (LiDAR) sensors, or both enable the collection of high spatial resolution three-dimensional (3D) quantitative geospatial data. This data may be used to support a variety of surveying and mapping activities, potentially with lower costs and greater safety than traditional survey methods. When using a camera, the technique is called Structure-from-Motion photogrammetry or UAS-SfM. In practice, there are important differences between UAS-SfM and UAS-LiDAR including measurement fidelity, operational considerations, post-processing workflows, and cost-effectiveness. With a lack of clear guidance on when UAS-SfM versus UAS-LiDAR is the best fit for a specific task, there is a need to evaluate the real-world performance capabilities and limitations of both technologies.

Language

  • English

Project

  • Status: Active
  • Funding: $543,334
  • Contract Numbers:

    0-7157

  • Sponsor Organizations:

    Texas Department of Transportation

    125 E. 11th Street
    Austin, TX  United States  78701-2483
  • Managing Organizations:

    Texas Department of Transportation

    125 E. 11th Street
    Austin, TX  United States  78701-2483
  • Project Managers:

    Glancy, Chris

  • Performing Organizations:

    Texas A&M University - Corpus Christi

    6300 Ocean Dr.
    Corpus Christi, Texas  United States  78412

    Texas A&M Transportation Institute

    Texas A&M University System
    3135 TAMU
    College Station, TX  United States  77843-3135
  • Principal Investigators:

    Starek, Michael

  • Start Date: 20220901
  • Expected Completion Date: 20240831
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01863351
  • Record Type: Research project
  • Source Agency: Texas Department of Transportation
  • Contract Numbers: 0-7157
  • Files: RIP, STATEDOT
  • Created Date: Nov 3 2022 5:08PM