Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery
arXiv:2606.25989v1 Announce Type: cross Abstract: Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Existing approaches struggle with severe domain shift across collection platforms, fine-grained visual similarity between closely related species, and uneven annotation granularity, where many specimens can only be identified to genus or a coarser taxonomic rank. We present a taxonomy-aware deep learning framework that aligns both the training loss and the inference rule with the hierarchical struct
Advances in deep learning and computational power enable more sophisticated AI applications, and the urgency for biodiversity monitoring is increasing due to climate change and environmental degradation.
This development represents a significant step towards scalable, automated environmental monitoring, which can inform more effective conservation policies and resource management.
The ability to accurately classify diverse marine species across varying imaging conditions fundamentally improves our capacity to understand and protect ocean biodiversity.
- · Marine biology research institutions
- · Environmental conservation NGOs
- · Oceanographic technology companies
- · Governments focused on marine policy
- · Manual data annotation services
- · Industries reliant on unregulated marine resource extraction
Improved accuracy and efficiency in marine species identification and biodiversity assessment using AI.
More robust and data-driven conservation strategies, potentially leading to new policy interventions and protected areas.
Enhanced global understanding of ocean health and ecosystem dynamics, influencing economic activities tied to marine environments.
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Read at arXiv cs.LG