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ORNL data helps FEMA distribute natural disaster relief faster

Oak Ridge National Laboratory (ORNL) researchers have mapped and characterized all structures within the United States and its territories to aid the Federal Emergency Management Agency (FEMA) in its response to disasters using deep learning.

When they say all structures, they mean every building that’s out there, including your house. Especially your house, because if FEMA knows what building needs what kind of help after a natural disaster, they can help a lot faster.

Extreme weather and natural disasters are happening with increasing frequency across the United States and its territories. Accurate and detailed maps are critical in emergency response and recovery.

ORNL said even before this fall’s devastating Hurricanes Fiona and Ian made landfall in Puerto Rico, Florida, and South Carolina, FEMA was working with researcher Lexie Yang and her team at ORNL to forecast potential damage and accelerate on-the-ground response using the previously mentioned map of buildings. It’s called USA Structures, a massive dataset of building outlines and attributes covering more than 125 million structures.

Over the past seven years, researchers in ORNL’s Geospatial Science and Human Security Division have mapped and characterized all structures within the United States and its territories to aid FEMA in its response to disasters. This dataset provides a consistent, nationwide accounting of the buildings where people reside and work.

“FEMA has GIS [geographic information systems] analysts that take our data and integrate it with post-disaster satellite imagery, aerial imagery, and information that first responders are collecting in the field,” ORNL’s Carter Christopher, Section Head for Human Dynamics in the Geospatial Science and Human Security Division said in a release.

How’d they do it? Through deep learning, which is a subset of machine learning, researchers processed images and compiled the data. Machine learning uses computers to detect patterns in massive amounts of data, then makes predictions based on what the computer learns from those patterns. In deep learning, the computing system creates its own algorithms rather than using algorithms developed and input by a human.

The existing dataset, paired with real-time impact information, can speed recovery by supporting damage assessments that property owners need in order to receive funds for rebuilding in days rather than weeks or months.

“That additional information, when available, makes the structures data more powerful. Is that square a house, a warehouse, or a church? Each of those has different implications in a disaster,” said Christopher.

Yang has seen growing interest from federal agencies, research organizations, local governments, and practitioners not only in using the data set – which is a free open source available to the public – but also in contributing and incorporating data from smaller local projects.

“This project is still evolving, and we expect to continue to have major updates to the current data,” she said. “We hope that more communities will use the data. It’s already proven to be valuable through FEMA’s work, but there may be other applications that are even more impactful.”

Learn more about how this data set is getting more detailed as a result of recent hurricanes and dig deeper into deep learning in this article from ORNL.

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