Crowd-sourced maps for natural disasters boosted by new algorithm
Humanitarian workers delivering aid to regions hit by natural disasters might find it a little easier to reach people most in need of help following new advances in crowd-sourced mapping technology, according to researchers.
Traditional maps often do not give rescue workers the information they need when disasters strike, such as which buildings and bridges have been destroyed.
Crowd-mapping, where volunteers on the ground send real-time information about which roads are open and where people could be trapped following earthquakes or hurricanes, has become increasingly popular with aid groups, U.S. researchers said.
To make the mapping process more efficient, researchers at the University of California and the University of Tennessee created a new algorithm that indicates which areas need detailed mapping first after a disaster.
“Online volunteers provide up-to-date geographic information that can help disaster response teams on the ground make more informed decisions,” said University of Tennessee geography professor Yingjie Hu.
“We wanted to make that process more efficient,” Hu said.
Originally from Sichuan, China, Hu began researching crowd-funded maps after a massive earthquake rocked his home province in 2008 killing more than 80,000 people.
Rescuers scrambled to save survivors but their efforts were hampered in some cases by a lack of up-to-date information about which roads were open to emergency vehicles, Hu said.
“If we could have applied this algorithm back then more lives could potentially have been saved,” he said.