FEMA Kilauea Volcano Affected Structures Assessment Aided by Deep Learning, Neural Networks

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July 26, 2018 | Originally published by Date Line: July 26 on

ORNL uses deep learning and models to train neural networks for extracting structure data / building footprints from satellite imagery.  As an example of how fast the system works, ORNL was able to processes the entire country of Yemen on their Titan supercomputer in less than two hours.

The Kilauea volcano in Hawaii began erupting in early May and has been doing so continuously since May 27, spewing lava across the Big Island and damaging more than 700 homes. Although there is a chance it could slow down or stop, it”s more likely that the flow will continue, according to a new report from the U.S. Geological Survey”s Hawaiian Volcano Observatory.

The lava from Kilauea has already had a devastating impact on residences, businesses, schools and hospitals. Officials at the Federal Emergency Management Agency have been getting insight into the buildings and infrastructure affected by the stream of molten rock with the help of Oak Ridge National Laboratory in Tennessee.

By analyzing satellite images of the area around the volcano and combining that information with property ownership data, officials can determine which buildings have been damaged and whether people are evacuating ahead of the lava”s approach, said Mark Tuttle, a project developer at ORNL.

FEMA reached out to the lab on May 9 because it had information on parcel locations but not building footprints, said Chris Vaughan, a geospatial information officer at FEMA. From satellite imagery, “Oak Ridge National Laboratory staff were able to extract building footprints for structures, at first starting around the Leilani Estates and Lanipuna Gardens area within the first week and then for most of the Big Island within two weeks,” Vaughan added.

ORNL receives its satellite images from DigitalGlobe, a satellite imaging and analysis company. Researchers then sharpen and adjust the images for perspective and select segments to help train a convolutional neural network that allows them to rapidly extract information from the images about buildings and other structures.

The number of segments needed to train a neural network depends on the size of the area researchers want to model and the diversity of the landscape within that area. Before deep learning, the process was done with basic segmentation, but it was difficult for researchers to generalize the data to a larger area, said Melanie Laverdiere, a research scientist at ORNL.

“With deep learning, we now have the power to create one model for a county or a state or a region, something that can be generalizable to a large geographic area,” she added.

For FEMA”s Hawaii project, however, the lab didn’t have to create a model — the final product of a trained neural network — because it already had one that would work. “We had a model we had developed in a [different] tropical region, and we were able to apply that to Hawaii,” Laverdiere said.

For the full USGS Report, see Cooperator Report to Hawaii Count Civil Defense, 15 July 2018.

For up to date ArcGIS maps and information on the Kilauea voclano, visit the ArcGIS story map FEMA-4366-DR-HI – Map Journal – ArcGIS.

For more information on ORNL”s supercomputers, visit Titan: Advancing the Era of Accelerated Computing and Summit: ORNL”s Next High Performance Supercomputer.

For more information about Esri, visit About Esri: We Pioneer Problem Solving with GIS.

For more information about ArcGIS, visit ArcGIS: the Mapping and Analytics Platform.

For more information on geographic information systems (GIS), visit What is GIS? A framework to Organize, Communicate, and Understand the Science of Our World.