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Home Artificial Intelligence

DARPA, USGS Name Winners of Mineral Assessment Competition; Stefanie Tompkins, David Applegate Quoted

by Jane Edwards
December 19, 2022
in Artificial Intelligence, News
DARPA, USGS Name Winners of Mineral Assessment Competition; Stefanie Tompkins, David Applegate Quoted

The Defense Advanced Research Projects Agency and the U.S. Geological Survey have announced the winners of a competition that examines technology platforms that use artificial intelligence and machine learning to help USGS automate the process of evaluating geologic maps of mineral deposits that are critical to the U.S. economy and national security.

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The AI for Critical Mineral Assessment Competition launched in August comes with the Map Georeferencing Challenge and the Map Feature Extraction Challenge, DARPA said Friday.

DARPA and USGS worked with MITRE and NASA’s Jet Propulsion Laboratory to evaluate submissions of 18 teams from industry and academia.

Canada-based Unchartered landed the top spot in the Map Georeferencing Challenge. Jataware was awarded the second prize, and Team Ptolemy, which includes members from MIT, Pennsylvania State University and the University of Arizona, secured the third place.

A team from University of Southern California Information Sciences Institute and University of Minnesota received the top prize in the Map Feature Extraction Challenge. Team ICM from the University of Illinois and Unchartered took second and third places, respectively, in the challenge.

“Critical minerals are essential to the national security supply chain, and as such, the agency is approaching the need from multiple angles,” said Stefanie Tompkins, director of DARPA and a 2022 Wash100 awardee.

Tompkins added the agency’s collaboration with USGS focuses on the identification of existing domestic resources.

USGS Director David Applegate said the competition has provided an opportunity to work with AI experts and improve its approach to assessing critical mineral resources.

“It has already led to incredible time savings in how we prepare data in a machine-readable format,” Applegate added.

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