New remote sensing development aid relief

When the earthquake hits, emergency personnel need to quickly know where their resources are being sent. Researchers at Michigan Tech University and his graduate students believe they can help.
Thomas Omen, an assistant professor in the Department of Geology and Mining Engineering and Science, is a new way to use remote sensing to quickly and more accurately assess losses. His tools: Crowdsourcing, or what he called "Volunteers of Power."
"The use of remote sensing image analysis for damage caused by earthquakes is a relatively new method of damage assessment," said Eumen. "It was developed after the earthquake in Sichuan in 2008, Haiti earthquake in New Zealand in 2010 and 2011."
Euromon said that with Web 2.0 technology and ubiquitous spectral remote sensing images with high resolution space (the smallest identifiable object of measurement), three components (sensors can record specific wavelengths) and time (how long can be obtained Image Sensor) - Crowdsourcing Damage Assessment. The benefits of doing so have been proven to damage the delivery speed two to three times the original estimate.
"In 10 days, close to 100,000 buildings assessed about 600 volunteers from 21 countries in Haiti's 300 square kilometers area," said Eumundo. "However, the detailed assessment also emphasizes the variability of this approach by emphasizing the limitations of accuracy impairment assessment and the expertise of volunteers in different populations."
To overcome this limitation, he proposes the automation of mission-critical tasks, the use of state-of-the-art machine learning and the role of image processing and transfer, volunteer volunteers to map and classify corrupted supervisory guidance from automatic task interpretation of manual image interpretation.
Recent examples in Haiti, New Zealand, and China include Oommen and his graduate students exploring this new population-based rapid damage assessment solution.
"Our system is called the backbone: build a crowdsourcing knowledge base extreme events," Euromon said. "Using researchers from all over the world, we want to improve the assessment of damage." The backbone needs to be able to adapt to different disasters based on the impairment of the object's characterization (in addition to pixel values ​​in spatial and contextual information) to fully distinguish between damage types.
The NSF Postgraduate Research Scholarship was awarded to the most recent Euromont and Daniel Cerminaro, civil engineering graduate students, and two years of undergraduate researcher Oumen developed initial results for the NSF. Their ongoing goals are still increasingly important before data is answered so that they can help those who need to be faster. Euromen said: "This will ultimately help save lives."

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