Sea Ice Image Semantic Segmentation Using Deep Neural Networks
Bibliographic record
Abstract
Semantic segmentation is the process of classifying pixels in an image into different classes. This method is quite established in autonomous vehicles, biomedical image processing, and remote sensing applications. In this paper, we evaluate the applicability of semantic segmentation for sea ice classification using image feeds from on-board an icebreaker. For this purpose, we evaluate SegNet and PSPNet101 neural network architectures to segment images into four classes: ice, ocean, vessel, and sky. The Nathaniel B. Palmer dataset, which captures 2-month footage of the icebreaker completing an Antarctic expedition was used. A subset of the dataset was labeled to generate a 240-image dataset achieving an accuracy of 97.8% classification for the 26 image test dataset. These results validate the applicability of deep learning methods for sea ice detection using images, which can be further improved by classifying the ice type to support marine navigation and mapping applications.
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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) | 0.001 | 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 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".