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

O'Neill-Dunne, Jarlath; Singh, Caesar; University of Vermont. Transportation Research Center

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

2012-12-01
By O'Neill-Dunne, Jarlath ; Singh, Caesar

[PDF-3.82 MB]

English

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Details:

Creators:

O'Neill-Dunne, Jarlath ; Singh, Caesar

O'Neill-Dunne, Jarlath ; Singh, Caesar Less -
Corporate Creators:

University of Vermont. Transportation Research Center
Corporate Contributors:

United States. Dept. of Transportation. Research and Innovative Technology Administration
Subject/TRT Terms:

[+]

Bridges And Other Structures Data And Information Technology Decision Support Systems Disasters And Emergency Operations Drones Emergency Management Floods Highways Image Analysis Maintenance And Preservation Remote Sensing Security And Emergencies
Resource Type:

Tech Report
Geographical Coverage:

United States ; Vermont

United States ; Vermont Less -
TRIS Online Accession Number:

01566589
Corporate Publisher:

University of Vermont. Transportation Research Center
Abstract:

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 acquiring imagery is far easier than extracting actionable information from it. An automated approach to damage assessment is needed, but traditional automated image analysis techniques are inadequate for identifying or characterizing road and bridge damage from high resolution imagery. 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; (2) to estimate the amount and type of fill material required for repairs using digital surface models derived from lightweight Unmanned Aerial Vehicles (UAV) 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. It is also proposed to collaborate with state departments of transportation to develop a web-based interface to share information derived from the CRS imagery.
Format:

PDF
Funding:

RITARS-12-H-UVM
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:e45bdf0f8084925d708a8ce851758cd2b1ccc98ab0f652bdb694393a68c6efad
Download URL:

https://rosap.ntl.bts.gov/view/dot/39910/dot_39910_DS1.pdf
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Rapid Exploitation of Commercial Remotely Sensed Imagery for Disaster Response & Recovery

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