DOI · DOI-0102 · record R2651

Enhancing U.S. critical mineral supply chains through AI and remote sensing mapping of legacy mine sites and tailings

Department of the Interior · USGS

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Energy and the Environment
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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

System outputs

A data set of mapped mines and/or mine tailings for two regions in different ecosystems.

Technology

AI classification: Reinforcement learningSource: Reinforcement Learning: AI trained through trial and error using rewards and penalties to optimize decision-making policies.
System name(s): Not reported
Custom-developed code: No
Public source code: Depending on outcome of the project, the code will be published.

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: k) None of the above
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported