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Guide for Efficient Geospatial Data Acquisition using LiDAR Surveying Technology

File Language:
English


Details

  • Corporate Creators:
    United States. Federal Highway Administration
  • Contributors:
  • Subject/TRT Terms:
  • Publication/ Report Number:
    FHWA-HIF-16-010
  • Resource Type:
    Booklet/Pamphlet
  • Geographical Coverage:
    United States
  • Corporate Publisher:
    United States. Federal Highway Administration
  • Abstract:
    State transportation agencies (STAs) rely on implementing innovative new processes and technology for managing roadway assets; LiDAR surveys are one of these key enabling technologies. LiDAR surveys comprise an ever-evolving set of technologies that allow for a rapid, yet very accurate, collection of roadway asset data through a single effort which, when specified, integrated, and used correctly, can result in efficient workflows for agencies. While STAs may already be collecting data for specific purposes, these efforts are often duplicated within various agency disciplines using a variety of collection methods and standards. LiDAR surveys help consolidate resources, thus maximizing funding and enhancing the accuracy and integration of information. While the process for implementing LiDAR surveys will depend on a number of factors — most notably the agency’s current pre-construction, post-construction, asset inventory practices, process maturity, internal technical resources, and available funding — this guide draws the decision makers’ attention to the key issues that must be addressed for the optimization of data collection for use in an enterprise digital data solution. These include developing information requirements for data collection and products to be delivered for specific purposes and disciplines within the agency, and implementing the data collection program itself.
  • Format:
    PDF
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:526be8482644cf7ff87ba690ec59b3c07013e079394d9744c1cf7cda843085f1ba0de09e7363a9e5c42b27756b94ed728b03919a2e30631984f44ac0b1e63078
  • Download URL:
  • File Type:
    Filetype[PDF - 2.22 MB ]
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