Applications of AI to LiDAR Data: Innovating Surveying Practices

Traditional surveying methods, such as leveling and total station surveys, rely heavily on manual processes for data collection and classification. These methods are time-consuming, labor-intensive, and susceptible to human error, which can lead to inaccuracies in the surveyed data. As a result, errors in point classification (e.g., distinguishing between pavement, vegetation, and utilities) can compromise the quality of information used for infrastructure planning and design. Additionally, these outdated techniques often lack the ability to capture comprehensive, high-density spatial data in a single survey, limiting their effectiveness for large-scale or complex projects. Consequently, relying solely on traditional tools can increase project costs, extend timelines, and necessitate costly revisions during later stages of construction and maintenance. This research explores the potential of Artificial Intelligence (AI), particularly object detection in Light Detection and Ranging (LiDAR) data, to identify infrastructure assets, mainly manholes and drop inlets and classify them with relevant information such as global positioning system (GPS) coordinates, elevation, and type. The study aims to advance surveying practices by leveraging AI-driven insights to enhance accuracy and efficiency. The primary goal of this research project is to leverage existing LiDAR data to enhance surveying practices through automation and AI integration. The specific objectives include: (1) identify the best AI model to detect selected infrastructure assets from LiDAR Data; (2) train and validate the selected AI models; and (3) develop guidelines for implementing the developed AI to analyze Ohio Department of Transportation (ODOT) LiDAR data and provide outputs in the corresponding formats: .LAS, .BIN, and CSV (COGO Points).

• Record URL:
  • https://www.dot.state.oh.us/Divisions/Planning/SPR/Research/reportsandplans/Lists/Final Reports All/Item/displayifs.aspx?List=47f3581d-f21c-403b-9358-fea0b008772b&ID=815&ContentTypeId=0x0100BD006C89430C884FB603A74E63BB6849

Language

• English

Project

• Status: Active
• Funding: $150,000.00
• Contract Numbers:

  42151

  123342

  136987

• Sponsor Organizations:

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Managing Organizations:

  Ohio Department of Transportation

  Research Program
  1980 West Broad Street
  Columbus, OH  United States  43223
• Project Managers:

  Fout, Vicky

• Performing Organizations:

  University of Cincinnati

  Civil Engineering Department, P.O. Box 210071, 741 Baldwin Hall
  Cincinnati, OH  United States  45221-0071
• Principal Investigators:

  Nazzal, Munir

• Start Date: 20250519
• Expected Completion Date: 20261120
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Asset management; Data collection; Implementation; Laser radar; Surveying
• Identifier Terms: Ohio Department of Transportation
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01950516
• Record Type: Research project
• Source Agency: Ohio Department of Transportation
• Contract Numbers: 42151, 123342, 136987
• Files: RIP, STATEDOT
• Created Date: Apr 3 2025 8:55AM