Enhancing U.S. critical mineral supply chains through AI and remote sensing mapping of legacy mine sites and tailings
Description
There is growing interest in producing critical minerals and energy-related commodities within the United States to reduce dependence on foreign sources. However, many legacy mine sites remain poorly documented, with unknown locations, extents, and conditions, limiting opportunities to reassess the potential for extracting valuable materials from existing tailings and waste piles.
Detailed example
A data set of mapped mines and/or mine tailings for two regions in different ecosystems.
AI / analytics pattern
Reinforcement Learning: AI trained through trial and error using rewards and penalties to optimize decision-making policies.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
develop a scalable, data-driven framework for identifying and prioritizing legacy mine sites with potential for critical mineral recovery. offers a cost-effective, repeatable method for improving national inventories of mine waste and tailings
Controls / human review
ATO: No; PIA: Not published