Iceberg Detection Based on the Swin Transformer Algorithm and SAR Imagery: Case Studies off Prydz Bay and the Ross Sea, Antarctic
Bibliographic record
Abstract
Abstract. Icebergs pose persistent hazards to maritime navigation and offshore operations. In Antarctica, grounded offshore icebergs may gradually melt, altering the local ocean stratification conditions. This in turn influences coastal ocean circulation, sea ice dynamics, and thermodynamics. Accurately identifying the spatiotemporal distribution of icebergs is essential for both maritime operations and oceanographic research. In this study, we developed an iceberg detection algorithm based on the Swin transformer model (IDAS-Transformer). The IDAS-Transformer, along with a support vector machine (SVM) and a residual network (ResNet18), was applied to four synthetic aperture radar (SAR) images acquired over Prydz Bay and the Ross Sea, which represented a landfast ice zone, a drift ice zone, and an open ocean. The coverage area of each image was 80 km × 80 km. Manual interpretation was employed to generate reference data for algorithmic evaluation purposes. The iceberg concentration, defined as the area occupied by icebergs per grid unit, along with the total number of icebergs and their average size, was introduced to provide a quantitative iceberg detection assessment. We found that the IDAS-Transformer performed well across various sea ice conditions, and a total of more than 800 icebergs were detected. Both the F1 scores and the kappa coefficients of the model exceeded 85 %. The total number of identified icebergs and their area presented mean biases of +4.13 % and +3.65 %, respectively. The IDAS-Transformer outperformed the other two tested algorithms. The sea ice concentration affects the iceberg detection process, with the main challenge being the separation of icebergs from similarly textured pack ice in complex ice-covered regions. Furthermore, distinguishing icebergs that are smaller than 160 m × 160 m among large ice floes remains difficult.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".