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Training and validation data from the AI for Critical Mineral Assessment Competition


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Goldman, M.A., Rosera, J.M., Lederer, G.W., Graham, G.E., Mishra, A., and Yepremyan, A., 2023, Training and validation data from the AI for Critical Mineral Assessment Competition: U.S. Geological Survey data release,


Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the competition are provided here, as well as competition details and baseline solutions. The data are derived from published sources and are provided to the public to support continued development [...]

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The training and validation data were provided to participants in the DARPA AI for Critical Mineral Assessment Competition to develop automated map georeferencing and feature extraction tools, two tasks that the USGS currently does manually using GIS software. The USGS prepares a wide range of maps (various scales, resolutions, and themes) when conducting mineral resource assessments. Manually performing these tasks is time- and labor-intensive. The purpose of the competition was to find innovative approaches to automatically georeference and extract features from diverse maps, with the overall goal of accelerating advances in critical mineral resource science and assessment.

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Type Scheme Key
DOI doi:10.5066/P9FXSPT1
USGS_Science_Topic_Keyword Economic Geology, Mineral Resources, Geography

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