Detection and Estimation of Inundation and Associated Risks using Traffic Monitoring Cameras and High-Resolution Flood Maps

During extreme flooding such as Hurricane Harvey, photo images from traffic monitoring cameras provide critical information, sometimes as the only reliable source, to identify whether or not a road is flooded. The advent of new image processing and filtering technologies has enabled us to extract extent of inundation from low-resolution photos with reasonable accuracy. Despite the high potential, however, the images from traffic monitoring systems have yet to be investigated to extract more accurate flood information using objective and automatic ways. The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and high-resolution flood maps under extreme precipitation conditions. A new Bayesian filtering method will be devised to detect occurrence of flooding and extract inundation extent from low-resolution images taken by the existing traffic monitoring cameras during the extreme events. High-resolution urban flood modeling will produce street-resolving flood maps based on multiple extreme precipitation frequencies. Capability of the filtering algorithm and the flood model will be demonstrated for the past extreme event (e.g. Hurricane Harvey) at a city scale (e.g. the Downtown Houston areas).

• Record URL:
  https://transet.lsu.edu/research-in-progress/
• Supplemental Notes:
  • 19SAUTA04

Language

• English

Project

• Status: Completed
• Funding: $110000
• Contract Numbers:

  69A3551747106

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Transportation Consortium of South-Central States (Tran-SET)

  Louisiana State University
  Baton Rouge, LA  United States  70803
• Project Managers:

  Melson, Christopher

• Performing Organizations:

  University of Texas at Arlington

  Department of Civil Engineering
  Box 19308
  Arlington, TX  United States  76019
• Principal Investigators:

  Noh, Seongjin

  Seo, Dong-Jun

  Ham, Suyun

• Start Date: 20190815
• Expected Completion Date: 20210215
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Algorithms; Cameras; Detection and identification systems; Floods; Image processing; Maps; Precipitation (Meteorology); Risk assessment; Traffic surveillance
• Identifier Terms: Hurricane Harvey, 2017
• Geographic Terms: Houston (Texas)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 01713583
• Record Type: Research project
• Source Agency: Transportation Consortium of South-Central States (Tran-SET)
• Contract Numbers: 69A3551747106
• Files: UTC, RIP
• Created Date: Aug 8 2019 7:57AM