Global automated extraction of bathymetric photons from ICESat-2 data based on a PointNet++ model
Bibliographic record
Abstract
We developed and tested a fully automated method to extract bathymetric photons globally from ICESat-2 ATLAS data by leveraging the PointNet++ model, which performs well for classification and segmentation of point clouds. Training data was collected from areas spanning > 100 degrees of latitude and encompassing a wide range of seafloor characteristics and variations in ICESat-2 data features. We explored a range of data compositions and model settings, and optimized them for model training. The final model obtained precision, recall and F1 scores of 0.9291, 0.9315 and 0.9303, respectively, and achieved an Intersection over Union (IoU) of 0.6351 for sites that contain detected seafloor. Model performance varies between test sites, with most errors occurring when there is a high density of noise photons near the seafloor. This study provides evidence for the global applicability of a trained PointNet++ model to automatically extract bathymetric photons from ICESat-2 data. The model is publicly available for use and further development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".