Prior-matched evaluation of operational Earth-observation classifiers: a three-number reporting method demonstrated on Sentinel-1 internal-wave detection
arXiv:2607.07146v1 Announce Type: new Abstract: The Internal Waves Service screens the Sentinel-1 Wave-mode archive for internal solitary waves, routing detections to experts whose adjudication time is the resource the effort exists to conserve. Because attention is the cost of error, precision leads. Its classifier was trained and reported at a one-to-one class balance, fixed before the operational rate could be known. That rate has since emerged at roughly one scene in twenty, and a balanced-test score badly overstates the precision a validator meets. A model that scores 0.794 balanced-test
The paper identifies a critical issue in AI model evaluation for real-world applications where class imbalance (prior) differs significantly from training conditions, which is common as AI moves from research to deployment.
Accurate and reliable AI system evaluation is crucial for their effective deployment in critical domains like Earth observation and defense, impacting resource allocation and trust.
This research proposes a new, more robust evaluation method that better reflects operational realities, leading to more trustworthy and useful AI deployments.
- · AI model developers
- · Earth observation services
- · Defense contractors using AI
- · AI models evaluated only on balanced datasets
- · Organizations relying on misleading AI performance metrics
Improved performance metrics for AI systems operating in imbalanced real-world environments become standard.
Increased efficiency in expert resource allocation as AI systems provide more reliable pre-screened data.
Accelerated integration of AI into complex operational systems due to higher confidence in performance guarantees.
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Read at arXiv cs.LG