Rapid Exploitation of Commercial Remotely Sensed Imagery for Disaster Response & Recovery

Natural disasters can severely impact transportation networks. In the hours and days following a major flooding event, knowing the location and extent of the damage is crucial for incident managers for a number of reasons: it allows for emergency vehicle access to affected areas; it facilitates the efficient rerouting of traffic; it raises the quality and reduces the cost of repairs; and it allows repairs to be completed faster, in turn reducing the duration of costly detours. Commercial Remote Sensing (CRS) imagery is increasingly being used in disaster response and recovery, but the ability to acquire CRS data far surpasses the ability to extract actionable information from it. An automated approach to damage assessment is needed, but traditional automated image analysis techniques are inadequate for identifying or characterizing transportation infrastructure damage from high-resolution CRS imagery. Furthermore, new CRS technologies, such as Unmanned Aircraft Systems (UAS) provide a novel approach to gathering imagery during a crisis in which traditional satellite and aerial systems are either cost prohibitive, ineffective, or unresponsive. The proposed project has two objectives: 1) to develop, calibrate and deploy a decision support system capable of identifying road and bridge damage from high- resolution commercial satellite images and; b) to estimate the amount and type of fill material required for repairs using digital surface models derived from lightweight Unmanned Aerial Vehicles (UAS) programmed to fly over damage road segments. This approach would employ state-of-the-art, object-based image analysis techniques, cost-based image matching, and other advanced computing techniques. The project also propose to collaborate with state departments of transportation to develop a web-based interface to share information derived from CRS Imagery.


    • English


    • Status: Completed
    • Funding: $234063
    • Sponsor Organizations:

      Department of Transportation

      1200 New Jersey Avenue, SE
      Washington, DC  United States  20590
    • Project Managers:

      Singh, Caesar

    • Performing Organizations:

      University of Vermont, Burlington

      Spatial Analysis Lab
      81 Carrigan Drive
      Burlington, VT  United States  05405
    • Principal Investigators:

      O'Neil-Dunne, Jarlath

    • Start Date: 20120901
    • Expected Completion Date: 20141231
    • Actual Completion Date: 20160728
    • USDOT Program: Advanced Research
    • Subprogram: Research

    Subject/Index Terms

    Filing Info

    • Accession Number: 01593955
    • Record Type: Research project
    • Source Agency: University of Vermont, Burlington
    • Files: RIP
    • Created Date: Mar 19 2016 3:08PM