Identifying significant environmental features using feature recognition.

Details

• Creators:
  White, Megan ; Zhu, Junfeng ; Blandford, Benjamin ; Grossardt, Ted
• Corporate Creators:
  University of Kentucky Transportation Center
• Corporate Contributors:
  Kentucky. Transportation Cabinet ; United States. Federal Highway Administration
• Subject/TRT Terms:
  • Laser radar
  • Geological surveying
  • Sinkholes
  • Geographic information systems
  • Automatic data collection systems
  • Software
  • Topography
  • Accuracy
  • Data processing
• Publication/ Report Number:
  KTC-15-18/SPR15-502-1F
• Resource Type:
  Tech Report
• Geographical Coverage:
  Kentucky
• Corporate Publisher:
  University of Kentucky Transportation Center
• Abstract:
  The Department of Environmental Analysis at the Kentucky Transportation Cabinet has expressed an interest in feature-recognition capability because it may help analysts identify environmentally sensitive features in the landscape, ; including those relating to historic preservation, archaeology, endangered species habitat, and geology. LIDAR Analyst ; and Feature Analyst are a pair of geoprocessing software packages that have been developed by Textron Systems. ; Using this software, users can use LIDAR data to identify finely-scaled user-specified features. The software’s ; automated feature extraction saves time that might otherwise be spent manually analyzing images and digitizing ; features. ; This report explores the capabilities and accuracy of this software by using LIDAR data to identify sinkholes ; throughout a small area in Kentucky. This report also discusses an alternative LIDAR-based geoprocessing methodology ; developed by the Kentucky Geological Society. The method relies on ArcGIS and Python scripting to identify sinkholes. ; The feasibility and applicability of these methodologies are compared, the workflow for each method is outlined, and the ; capabilities and limitations of each are noted. Sample results—the identification of sinkholes—from each methodology ; are presented. The research team found the batch processing capability built into LIDAR and Feature Analyst adequate ; and beneficial for smaller projects, such as projects that prioritize the extraction of buildings, trees, and forest regions.
  More +
• Format:
  PDF
• Funding:
  SPR15-502
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29542/dot_29542_DS1.pdf
• File Type:
  [PDF - 5.69 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:bfda9eda2b58614470464c28f6bc238130f3f2183dd6c65fe7897241042c1c25