Navigating the Annotation Bottleneck: Active Learning for Scalable Maritime Data Analytics
Bibliographic record
Abstract
The analysis of maritime vessel trajectories is critical for enhancing safety, security, and operational efficiency in global shipping. However, large-scale manual annotation of Automatic Identification System (AIS) data remains a significant bottleneck due to its labour-intensive and costly nature. This paper introduces a novel Active Learning (AL) framework for trajectory classification, designed to minimize annotation effort while maintaining high model performance. The approach leverages time-series transformer embeddings to generate compact, temporally informed representations of vessel tracks, which are clustered to enable diversity-aware sampling. Four AL strategies - random, uncertainty-based, diversity-based, and a hybrid uncertainty-diversity approach - are evaluated within a scalable pipeline that integrates a vector database for efficient retrieval and a lightweight neural network for classification. Experiments on 5,000 vessel tracks demonstrate that the hybrid strategy achieves a Macro F1-score of 0.736 and Macro Precision of 0.784, while reducing labelling requirements by$\sim 40 {\%}$compared to random sampling. Through 70 AL iterations, the hybrid sampling method balances exploration (diverse featurespace coverage) and exploitation (high-uncertainty sample selection), accelerating learning convergence. The framework's scalability is validated via integration with vector search infrastructure, enabling real-time deployment. Beyond maritime data analytics, this work generalizes to other time-series domains, such as air traffic management (ADS-B), IoT sensor streams, logistics, and Mobile Advertising Identification (MAID) data.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".