Annotation bottleneck
Current factStudy scale was capped by how much imagery/audio/video a trained human could annotate.
Deep-learning annotation (CoralNet benthic annotators; FathomNet-trained ROV video models; NOAA VIAME fish counting) automates first-pass labelling; humans verify rather than label from scratch.
Large-team-scale analysis now achievable by individuals or small labs.
Pattern-findingResearchingContinuous long-term datasets become tractable instead of snapshot surveys.
AnalyzingObserving
- CoralNet (UCSD): ~3,000 users, >65M annotations
- FathomNet/MBARI: ~81% effort reduction, ~10x labelling rate
- NOAA VIAME
Low risk on direction; moderate on magnitude (reach into the long tail of under-described taxa is uncertain).
Best on well-catalogued taxa; human verification standard; rare/cryptic species still need experts.