Michigan Technological University (MTU) researchers have developed BACKBOnE (Building a Crowdsourced Knowledge Base of Extreme Events), a system that uses crowdsourcing to quickly and more accurately assess the damage from natural disasters.
"The use of crowdsourcing to analyze earthquake-induced damages in remotely-sensed imagery is a relatively new damage assessment approach," says MTU professor Thomas Oommen. "It was developed in the wake of the 2008 Sichuan earthquake and formalized during the 2010 Haiti and 2011 New Zealand earthquakes." Oommen notes that the advent of Web 2.0 technologies and the ubiquity of free remote-sensing images that possess three components of high resolution have made crowdsourced damage assessment possible.
However, detailed ground assessment highlighted the limitation of the approach and the variability in accuracy of damage assessment because of differing expertise of the crowd volunteers. To overcome this limitation, Oommen says the researchers automated key tasks using machine-learning and image-processing algorithms and shifted the role of the volunteer crowd from manual image interpretation to supervised guidance of the automated tasks for mapping and classifying damage.
From Michigan Tech News
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