Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition

Margaret A. Goldman; Graham W. Lederer; Joshua Mark Rosera; Garth E. Graham; Asitang Mishra; Alice Yepremyan

doi:10.1016/j.acags.2025.100274

Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition

Applied Computing and Geosciences

By: Margaret A. Goldman, Graham W. Lederer, Joshua Mark Rosera, Garth E. Graham, Asitang Mishra, and Alice Yepremyan

https://doi.org/10.1016/j.acags.2025.100274

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Abstract

The U.S. Geological Survey (USGS), Defense Advanced Projects Research Agency (DARPA), NASA Jet Propulsion Laboratory (JPL), and MITRE ran a 12-week machine learning competition aimed at accelerating development of AI tools for critical mineral assessments. The Artificial Intelligence for Critical Mineral Assessment Competition solicited innovative solutions for two challenges: 1) automated georeferencing of historical maps, and 2) automated feature extraction from historical maps. Competitors used a new dataset of historical map images to train, validate, and evaluate their models. Automated georeferencing pipelines attained a median root-mean square error of 1.1 km. Prompt-based extraction (i.e., with user input) of polygons, polylines, and points from geologic maps yielded median F1-scores of 0.77, 0.56, 0.35, respectively. Geologic maps pose numerous challenges for AI workflows because they vary significantly. However, despite its short duration, the competition yielded promising results that have since spurred further innovation in this area and led to the development of new AI tools to semi-automate key, time-consuming parts of the assessment workflow.

Suggested Citation

Goldman, M.A., Lederer, G.W., Rosera, J.M., Graham, G.E., Mishra, A., Yepremyan, A., 2025, Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition: Applied Computing and Geosciences, v. 27, 100274, 15 p., https://doi.org/10.1016/j.acags.2025.100274.

Additional publication details
Publication type	Article
Publication Subtype	Journal Article
Title	Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition
Series title	Applied Computing and Geosciences
DOI	10.1016/j.acags.2025.100274
Volume	27
Year Published	2025
Language	English
Publisher	Elsevier
Contributing office(s)	Geology, Energy & Minerals Science Center
Description	100274, 15 p.