Automated georeferencing and feature extraction of geologic maps and mineral sites

Graham W. Lederer; Joshua Mark Rosera; Margaret A. Goldman; Garth E. Graham; Asitang Mishra; Amanda Towler; Brian Wilson; Dustin Graf; Michael Milano; Elizabeth Roberts; Gabrielle Hedrick; Carsten Oertel; Anastassios Dardas; Thomas McEntee

Automated georeferencing and feature extraction of geologic maps and mineral sites

By: Graham W. Lederer, Joshua Mark Rosera, Margaret A. Goldman, Garth E. Graham, Asitang Mishra, Amanda Towler, Brian Wilson, Dustin Graf, Michael Milano, Elizabeth Roberts, Gabrielle Hedrick, Carsten Oertel, Anastassios Dardas, and Thomas McEntee

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Abstract

The predictive power of mineral prospectivity analysis depends on high quality, spatially accurate, analysis-ready datasets. Of paramount importance are geologic maps and mineral site data, but the state of readiness for utilizing these datasets remains sub-optimal for advanced computational techniques. As the U.S. Geological Survey (USGS) fulfils its mission to map the distribution of critical mineral commodities, non-georeferenced maps held within historical collections represent rich sources of input data. Through a series of machine learning challenges organized by the Defense Advanced Research Projects Agency (DARPA) in collaboration with the USGS, significant progress has been made in accelerating data ingestion, processing, and preparation tasks that enable mineral prospectivity mapping and mineral resource assessment workflows. Specifically, two tasks that previously required time-intensive human effort, 1) georeferencing map images, and 2) legend-based feature extraction from map images, are discussed.

Suggested Citation

Lederer, G.W., Rosera, J.M., Goldman, M.A., Graham, G.E., Mishra, A., Towler, A., Wilson, B., Graf, D., Milano, M., Roberts, E., Hedrick, G., Oertel, C., Dardas, A., McEntee, T., 2023, Automated georeferencing and feature extraction of geologic maps and mineral sites, in Abstract proceedings: MinProXT 2022, Freiberg, Germany, November 1-3, 2022, p. 8-10.

Additional publication details
Publication type	Conference Paper
Publication Subtype	Conference Paper
Title	Automated georeferencing and feature extraction of geologic maps and mineral sites
Year Published	2023
Language	English
Publisher	Geological Survey of Finland
Contributing office(s)	Geology, Energy & Minerals Science Center
Description	3 p.
Larger Work Title	Abstract proceedings: MinProXT 2022
First page	8
Last page	10
Conference Title	Mineral Prospectivity and Exploration Targeting (MinProXT 2022)
Conference Location	Freiberg, Germany
Conference Date	November 1-3, 2022