This standard provides a unified and quantifiable framework for evaluating the quality of mineral exploration datasets that are used for Artificial Intelligence (AI). The standard specifies: (1) Data classification as a multi-dimensional classification system for mineral exploration datasets based on business scenarios, data sources, modalities, security levels, and model training stages; (2) Quality evaluation metrics as a comprehensive metric system that comprises seven core dimensions: diversity, factuality, security, timeliness, professional depth, completeness, and scenario applicability; (3) Evaluation methods that comprise both direct evaluation mechanisms and indirect evaluation mechanisms which are supported by various statistical sampling strategies; and (4) Evaluation procedures that include preparation, execution, analysis, and summary.
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Status
- Active PAR
- PAR Approval
- 2026-06-04
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
AI 4 Geosciences - AI Data Quality for Geosciences
- IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Shi Luo
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