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Proceedings Paper

Supporting relief efforts of the 2010 Haitian earthquake using an airborne multimodal remote sensing platform
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Paper Abstract

The small island nation of Haiti was devastated in early 2010 following a massive 7.0 earthquake that brought about widespread destruction of infrastructure, many deaths and large-scale displacement of the population in the nation's major cities. The World Bank and ImageCat, Inc tasked the Rochester Institute of Technology's (RIT) Wildfire Airborne Sensor Platform (WASP) to gather a multi-spectral and multi-modal assessment of the disaster over a seven-day period to be used for relief and reconstruction efforts. Traditionally, private sector aerial remote sensing platforms work on processing and product delivery timelines measured in days, a scenario that has the potential to reduce the value of the data in time-sensitive situations such as those found in responding to a disaster. This paper will describe the methodologies and practices used by RIT to deliver an open set of products typically within a twenty-four hour period from when they were initially collected. Response to the Haiti disaster can be broken down into four major sections: 1) data collection and logistics, 2) transmission of raw data from a remote location to a central processing and dissemination location, 3) rapid image processing of a massive amount of raw data, and 4) dissemination of processed data to global organizations utilizing it to provide the maximum benefit. Each section required it's own major effort to ensure the success of the overall mission. A discussion of each section will be provided along with an analysis of methods that could be implemented in future exercises to increase efficiency and effectiveness.

Paper Details

Date Published: 20 May 2011
PDF: 9 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80480F (20 May 2011); doi: 10.1117/12.883435
Show Author Affiliations
Jason W. Faulring, Rochester Institute of Technology (United States)
Donald M. McKeown, Rochester Institute of Technology (United States)
Jan van Aardt, Rochester Institute of Technology (United States)
May V. Casterline, Rochester Institute of Technology (United States)
Brent D. Bartlett, Rochester Institute of Technology (United States)
Nina Raqueno, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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