Geomaticians

Deep Learning Underlies Geographic Dataset Used In Hurricane Response

Deep Learning Underlies Geographic Dataset Used In Hurricane Response
As Hurricane Fiona made landfall as a Category 1 storm in Puerto Rico on Sept. 18, 2022, some areas of the island were inundated with nearly 30 inches of rain, and power to hundreds of thousands of homes was knocked out. Only 10 days later, Hurricane Ian, a Category 4 storm and one of the strongest and most damaging storms on record, landed in Lee County, Florida, leveling homes and flooding cities before moving up the coast and making landfall again as a Category 1 storm in South Carolina. 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. 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. The agency requested two new attributes for the data the same day Fiona made landfall: occupancy types and addresses, critical information in speeding federal emergency funds to households and businesses.