ArticleScientific data2025
BenthicNet: A global compilation of seafloor images for deep learning applications.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Improving rare-class detection in deep-sea imagery via generative augmentation with stable diffusion.Scientific reports · 2026Article
- Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision.Journal of imaging · 2026Review
- Rapid consistent reef surveys with DeepReefMap.Scientific reports · 2025Article
- Is AI currently capable of identifying wild oysters? A comparison of human annotators against the AI model, ODYSSEE.Frontiers in robotics and AI · 2025Article
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Authors and funding
29 authors.
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Abstract
Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 3.1 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for reuse.
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Registered trials
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