In-situ Sea Ice Detection using DeepLabv3 Semantic Segmentation
Bibliographic record
Abstract
Identifying type and concentration of sea-ice is a crucial activity performed to assess navigational risk in arctic waters and support daily ice charting activities of the Canadian Ice service. Recent advancements in deep neural networks offer an automated solution to ice charting with retraining capability to continuously improve the system using new data. In this work, we evaluate and compare two popular deep learning based semantic segmentation architectures: Pyramid Parsing Network (PSPNet101) and Deep Labelling for semantic image segmentation (Deeplabv3) for sea ice total concentration detection. The datasets are created using the Nathaniel B. Palmer dataset imagery with four main classes: ice, sky, ocean, and ship, and divided into training, validation, and test sets. The overall mean Intersection of Union (mIoU) performance of Deeplabv3 is 90.21 while for PSPNet101 reported an mIoU of 90.1. The main advantage of the Deeplabv3 model is its compatibility for mobile devices with faster inference speed than the PSPNet101 architecture. The results demonstrate an average inference speed of 0.08s for Deeplabv3 while the PSPNet requires more than 1.9s per image to generate results on a Jetson AGX Xavier deep learning navigation box. Furthermore, the model is capable of successfully performing on mobile devices after Tensorflow Lite conversion with an average execution time of 12s per image on an off the shelf Android device.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".