INVESTIGATING FULLY CONVOLUTIONAL NETWORK TO SEMANTIC LABELLING OF BATHYMETRIC POINT CLOUD
Bibliographic record
Abstract
Abstract. The benefit of autonomous vehicles in hydrography is largely based on the ability of these platforms to carry out survey campaigns in a fully autonomous manner. One solution is to have real-time processing onboard the survey vessel. To meet this real-time processing goal, deep learning based-models are favored. Although Artificial Intelligence (AI) is booming, the main studies have been devoted to optical images and more recently, to LIDAR point clouds. However, little attention has been paid to the underwater environment. In this paper, we present an investigation into the adaptation of deep neural network to multi-beam echo-sounder (MBES) point cloud in order to classify sea-bottom morphology. More precisely, the paper investigates whether fully convolutional network can be trained while using the native 3D structure of the point cloud. A preprocessing approach is provided in order to overcome the lack of adequate training data. The results reported from the test data sets show the level of complexity related to natural, underwater terrain features where a classification accuracy no better than 65% can be reached when 2 micro topographic classes are used. Point density and resolution have a strong impact on the seabed morphology thereby affecting the classification scheme.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".