3D colored point cloud classification of a deep-sea cold-water coral and sponge habitat using geometric features and machine learning algorithms
Bibliographic record
Abstract
Classification of benthic habitats in the deep sea is instrumental in managing and monitoring marine ecosystems as it provides distinct units for which changes can be quantified over time. These applications require automatic classification approaches with reasonable accuracy to ensure efficiency and robustness. The use of 3D point clouds is currently emerging in deep-sea benthic classification as it allows for high-resolution representation of the 3D structure (i.e., geometry), texture, and composition of complex benthic habitats such as those created by structure-forming cold-water corals. Point clouds were derived from remotely operated vehicle video surveys of three vertical walls (depth range 1400–1900 m) along the Charlie-Gibbs Fracture Zone, North Atlantic. In addition to RGB values, this research incorporated nine geometric features derived from structure-from-motion 3D point clouds to classify coral and sponge colonies. Three unsupervised (k-means (KM), fuzzy c-means (FCM), and Gaussian mixture model (GMM)) and three supervised (decision tree (DT), random forest (RF), and linear discriminant analysis (LDA)) machine learning (ML) algorithms were compared and assessed for accuracy and reliability. The ML classifiers were used to build full-coverage seafloor predictions for three classes, namely, seabed, sponges, and corals. The KM, GMM, and FCM achieved an average overall accuracy of 74.87%, 71.94%, and 70.77%, respectively, while the RF, LDA, and DT achieved 84.50%, 84.01%, and 79.90%, respectively. Overall, the supervised ML classifiers outperformed the unsupervised ML classifiers. In particular, the RF classifier demonstrated the highest overall classification accuracy and F1-score for individual classes, with an average of 89.09%, 67.12%, and 41.60% for the seabed, sponges, and corals, respectively. In addition, the spatial coherence of the point clouds was considered and improved the results’ overall accuracy and F1-score by up to 9% and 12%, respectively. Results showed that incorporating geometric features, traditionally employed in terrestrial and shallow-water LiDAR surveys, in combination with RGB values is suitable for high-resolution deep-sea benthic 3D point clouds classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".