A Weakly Supervised Learning Approach for Sea Ice Stage of Development Classification From AI4Arctic Sea Ice Challenge Dataset
Bibliographic record
Abstract
Deep learning (DL)-based fully supervised approaches have demonstrated remarkable performance in sea ice classification, showcasing their potential for highly accurate results. However, their reliance on high-resolution labels poses a formidable challenge, as obtaining such data can be a difficult task. In contrast, our method based on weakly supervised learning excels by operating with lower-resolution polygon labels while still achieving outstanding performance. This approach enables precise pixel-level classification of ice stage of development (SOD) by learning from region-based labels embedded within expert-annotated ice charts. During training, region-based loss functions are introduced to quantify the disparity between predicted tensors describing SOD distributions and label tensors derived from ice charts. We leverage the AI4Arctic Sea Ice Challenge Dataset, comprising over 500 Sentinel-1 synthetic aperture radar (SAR) images, ancillary multisource data, and corresponding ice charts, for model training and evaluation. Visual interpretation and numerical analysis reveal that our weakly supervised method outperforms the fully supervised U-Net benchmark. It yields more accurate SOD predictions, significantly enhancing mapping resolution and class-wise accuracy. This methodology marks a critical step forward in the quest for automated operational sea ice mapping.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".