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REVIEW 4 major objections 4 minor 46 references

EnvShip: A Unified Framework for Context-Aware and Cross-Region Vessel Trajectory Forecasting

T0 review · 4 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read EnvShip-Bench is built to make vessel trajectory forecasting comparable: a fixed 30→30 protocol applied to public AIS data from Denmark and the US, with per-sample environmental and nearby-vessel context.

desk verdict The body is a plausible short-term maritime benchmark, but the abstract oversells multi-region, long-horizon, and cross-region results that don't exist in the text, and the vessel-disjoint split claim is undocumented. read the letter →

arxiv 2606.15240 v2 pith:6Z4EWXCA submitted 2026-06-13 cs.LG

Kun Ma , Qilong Han , Chengjing Song , Jingzheng Yao , Hao Wang , Changmao Wu This is my paper · ORCID
classification cs.LG
keywords vesseltrajectorypredictionAISmaritimebenchmarkcontext-awareforecastingenvironmentalcontextsocialshort-term
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish a common testbed for short-term vessel trajectory prediction, arguing that the field's core problem is not missing models but missing comparability: every study uses its own preprocessing, horizons, and evaluation, so results cannot be trusted across papers. It introduces EnvShip-Bench, a benchmark built from raw AIS archives of two public sources through a single pipeline, under a uniform protocol of 10 minutes of observation and 10 minutes of prediction at 20-second resolution, in vessel-centric local metric coordinates. The release is layered—a large core set, a quality-first compact subset, and synchronized environmental and social-context extensions—so that trajectory-only, environment-aware, and interaction-aware forecasting share one split and one metric. If the benchmark is adopted, published ADE/FDE numbers would mean the same thing everywhere, and context-aware maritime forecasting could be studied systematically. The initial baselines support the paper's claim that the benchmark is neither trivial nor saturated: simple recurrent models stay competitive, while kinematic augmentation and context help only in specific configurations.

What carries the argument

The load-bearing mechanism is the 30→30 protocol: each sample contains 30 observed and 30 future positions sampled every 20 seconds (10 minutes each), placed in vessel-centric local metric coordinates with the last observed point as origin. This single choice converts heterogeneous AIS archives into one evaluation space and makes cross-region comparability possible. Around it, the layered release does the work: the core set preserves motion diversity, the compact subset's quality scoring, stratified sampling, and per-window redundancy caps enable efficient and reproducible experiments, and the context extensions (vector/raster environmental layers plus target-centric neighbor descriptors) le

What would settle it

Compute the overlap of vessel identifiers between the released training and test sets; if any vessel has windows in both, the comparability claim fails. Alternatively, train a trivial model that memorizes vessel-ID-to-output mappings and see whether its test ADE is suspiciously low.

Watch

Extended reading notes

Core claim

The paper's central claim is that a benchmark, not a model, is what the field needs next. EnvShip-Bench organizes raw AIS data from two public archives into a standardized forecasting setting: 30 observed positions and 30 future positions at 20-second intervals, expressed in vessel-centric local metric coordinates so that Danish and US waters are directly comparable. The benchmark is released in layers: a large-scale core set, a curated 40,000-sample compact subset with stricter motion screening and redundancy control, and context extensions that align each sample with shoreline/occupancy raster and vector layers and with nearby-vessel descriptors such as relative distance, velocity, CPA, an

Load-bearing premise

The benchmark's fair-comparison claim depends on no vessel's trajectories appearing in both training and test; the paper describes per-window and per-segment caps but never states vessel-level disjointness, so a vessel present in both splits would inflate reported errors.

Editorial extensions

If this is right

  • If the benchmark is adopted, published ADE/FDE results become meaningful across papers because they all follow the same protocol, split, and coordinate system.
  • The three task variants let researchers isolate the contribution of environmental versus nearby-vessel context without changing the prediction target or the evaluation metric.
  • The baseline numbers give future work a concrete reference: simple recurrent models are hard to beat, and kinematic augmentation is not universally helpful.
  • The benchmark's documented long-tail imbalance (roughly 92% open-water, weak-interaction samples) points to rebalancing and scene-centric subsets as the next steps.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The fair-comparison claim would be directly testable by releasing vessel-ID overlap statistics between train and test; absent that, the split-integrity assumption remains unverified.
  • A scene-centric subset, which the paper flags as future work, would likely be the variant that makes environmental context matter most, since port and narrow-channel cases are exactly where shoreline structure constrains motion.
  • The social-context extension with CPA/TCPA descriptors could be reused beyond trajectory forecasting, for encounter-rate and collision-risk studies, because it provides per-sample encounter geometry aligned with trajectories.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper presents EnvShip-Bench, a benchmark for short-term vessel trajectory forecasting built from DMA and NOAA AIS data under a 30-observation/30-prediction protocol at 20-second sampling. It consists of a large-scale core release, a quality-first compact subset (40,000 samples in Table 1), and synchronized environmental and social context extensions. Initial deterministic baselines (Seq2Seq, GRU, Bi-GRU, LSTM, Bi-LSTM, TrAISformer, Social-LSTM, Social-LSTM+Env) are reported with ADE/FDE. The body claims the benchmark provides a standardized, extensible, context-aware foundation; the arXiv abstract additionally claims a multi-region framework with 330k/106k samples and cross-region results, none of which appear in the body.

Significance. A well-documented, public AIS forecasting benchmark with standardized splits and aligned context would be a valuable community asset, and the effort to unify DMA/NOAA data under one protocol is commendable. The layered design (core / compact / context extensions) is sensible and the initial baseline range is appropriate. However, the manuscript currently does not establish the central validity of the benchmark: split construction is under-specified, curation thresholds are not reported, baseline results have no variance information, and the abstract promises substantially more than the body delivers. The potential usefulness of the resource does not compensate for the missing evidence.

major comments (4)
  1. [Abstract vs. body] The arXiv abstract claims a multi-region framework covering Denmark, the US, Greece, and Norway, with 330,000 short-term and 106,857 long-horizon samples, cross-region training, and scene-type-dependent context gains. The body (Sections 1, 3.1, 4.4) describes only DMA and NOAA short-term 30→30 forecasting and reports no long-horizon, no Greek/Norwegian data, and no cross-region experiments. The abstract's claim that environmental context yields largest gains in coastline-constrained scenes is not tested anywhere; Section 4.5 states that 92% of the compact subset is open-water. The manuscript must be aligned: either the missing data and experiments must be provided, or the abstract must be reduced to what the body actually delivers.
  2. [§3.3, §4.3] The paper does not show that the train/validation/test split is vessel-disjoint. Section 3.3 caps retained windows per vessel and enforces minimum spacing between windows from the same segment, but these are intra-split overlap controls; they do not prevent the same vessel from appearing in both training and test. Section 4.3 only says all models use the same train/validation/test split. Since windows from one vessel are strongly correlated, cross-split vessel leakage would inflate ADE/FDE and make model comparisons meaningless. Please state the split construction rule and report the number of vessels per split and the number/proportion of vessels that appear in more than one split; if leakage exists, the benchmark must be rebuilt.
  3. [§3.2, §3.3] The pipeline description is too underspecified to be reproducible. Section 3.2 mentions conservative global motion filters, excessive temporal gaps, prolonged low-speed behavior, negligible displacement, and residual geometric anomalies without giving thresholds or formulas. Section 3.3 introduces heuristic quality and difficulty scores, caps, and minimum spacing, but does not define the scores, the cap values, the spacing value, or the stratification bins. For a benchmark paper, these are load-bearing: readers cannot verify the quality-first claim or apply the same curation. Please provide a dataset card with all thresholds, score definitions, and the resulting counts (samples, vessels, segments) per split for the core and compact releases.
  4. [§4.3, Table 2] All baseline numbers in Table 2 are single-run values. No random seeds, learning rates, batch sizes, model sizes, or validation-based hyperparameter choices are reported, although the abstract mentions analysis over random seeds. The conclusion that the benchmark is not biased toward a single model family depends on the relative ordering of Seq2Seq (59.57 ADE) and GRU (59.72 ADE) and TrAISformer (130.34 FDE) vs Seq2Seq (131.43 FDE); differences of 0.15 m and 1.09 m are almost certainly within run-to-run variance. Please run at least 3-5 seeds for each configuration and report mean±std, or equivalently provide confidence intervals.
minor comments (4)
  1. [Title / naming] The arXiv metadata calls the system 'EnvShip,' while the full text uses 'EnvShip-Bench.' Use a single name throughout to avoid confusion.
  2. [§4.5] The second paragraph begins with lowercase 'second' after a period; should be 'Second'.
  3. [§3.4.2] The 5000 m neighbor-retrieval radius is given, but there is no analysis of sensitivity to this radius or the definition of 'interaction-rich cases'; please clarify how the interaction flags are derived.
  4. [Data availability] The abstract gives a Hugging Face link and the conclusion gives a GitHub link; please unify the distribution links and include a machine-readable dataset card with checksums, schema, and license information so the release can be verified.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: benchmark results are measured on a fixed split, not derived from the benchmark's own definitions.

full rationale

EnvShip-Bench is a dataset/benchmark contribution, not a derived prediction. The paper's only quantitative outputs are baseline ADE/FDE measurements obtained by training models on a released training split and evaluating on the test split (Section 4.3, Table 2). These are observations, not quantities that reduce to the benchmark's construction by definition. The construction steps (screening, resampling, sliding windows, context alignment) define the data, and the baseline numbers measure models on that data; there is no fitted parameter that is later renamed as a prediction. The authors' prior self-citations ([15], [16]) are diffusion-model papers used as related work and are not load-bearing for the benchmark's validity. The only concern is an internal inconsistency: the arXiv abstract claims vessel-disjoint splits and multi-region/cross-region results that are not documented in the body (Section 3.3 describes only per-vessel/per-segment window caps and spacing, not cross-split vessel exclusion), and the 330k/106,857 sample counts do not appear in the manuscript. This is a missing-support or overclaim problem, not circularity. No cited uniqueness theorem or ansatz is imported from prior work; the benchmark claims are evaluated against external AIS resources in Table 1. Under the stated hard rules, no circular step can be exhibited, so the score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The benchmark rests on design choices (protocol, radius, curation heuristics) and on assumptions about AIS data quality and split integrity. No new physical entities are introduced.

free parameters (5)
  • Forecasting protocol (T_h=30, T_f=30, 20s sampling) = 10 min observation / 10 min prediction / 20 s
    Hand-chosen standardization that defines every sample; no task analysis justifying this specific horizon/resolution.
  • Neighbor retrieval radius = 5000 m
    Fixed radius for target-centric social context in §3.4.2; no sensitivity analysis is provided.
  • Quality/difficulty score heuristics = not specified
    Used in §3.3 to curate the compact subset, but the heuristic formulas, weights, and thresholds are not given.
  • Motion screening thresholds = not specified
    §3.2 applies 'conservative global motion filters' and ship-type-aware checks, but the actual thresholds are not reported.
  • Compact subset size = 40,000 samples
    Target size for the curated subset; the criterion for choosing exactly this size is not discussed.
assumptions (4)
  • domain assumption Raw AIS records from DMA and NOAA are sufficiently accurate and dense for 20-second resampling and interpolation.
    §3.2 relies on interpolating short gaps onto a 20-second grid; if AIS gaps or interpolation distort true motion, the benchmark measures artifacts.
  • domain assumption Vessel-centric local planar coordinates provide a distortion-free common evaluation space.
    §3.1 assumes local metric coordinates avoid scale distortion, but no projection or curvature error analysis is provided.
  • ad hoc to paper The fixed train/validation/test split is free of vessel/segment leakage.
    The paper's fair-comparison claim depends on this, but §3.3 only describes per-vessel window caps and spacing, not explicit vessel-disjoint splits.
  • ad hoc to paper Heuristic quality and difficulty scores identify 'clean yet nontrivial' samples.
    §3.3 uses these scores for curation, but there is no external validation that the scores correlate with forecasting usefulness.

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Cite this review

Pith. "Pith review of EnvShip: A Unified Framework for Context-Aware and Cross-Region Vessel Trajectory Forecasting." pith.science (2026). https://pith.science/paper/6Z4EWXCA

@misc{pith2026260615240,
  author       = {Pith},
  title        = {Pith review of: EnvShip: A Unified Framework for Context-Aware and Cross-Region Vessel Trajectory Forecasting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6Z4EWXCA}},
  note         = {Machine review of arXiv:2606.15240}
}
read the original abstract

Accurate vessel trajectory forecasting is essential for maritime situational awareness, navigation safety, traffic management, and autonomous navigation. Public Automatic Identification System (AIS) archives have enabled extensive research in this area, yet results remain difficult to compare because existing studies use incompatible preprocessing pipelines, forecasting horizons, data splits, coordinate systems, contextual inputs, and evaluation settings. We present EnvShip, a unified multi-region framework for context-aware and cross-region vessel trajectory forecasting. EnvShip applies a fixed and reproducible pipeline to public AIS data from Denmark, the United States, Greece, and Norway, and defines two standardized forecasting tracks spanning short- and long-horizon settings. From large-scale processed data, we curate 330{,}000 short-term and 106{,}857 long-horizon samples through strict motion screening, vessel-category and difficulty stratification, redundancy control, and vessel-disjoint splits. Each sample is aligned with environmental and neighboring-vessel context, together with weather and sea-state variables where available. We evaluate representative methods under in-domain and cross-region protocols, with analyses across prediction difficulty, scene type, and random seeds. Results show that environmental context provides the largest gains in coastline-constrained scenes, whereas neighboring-vessel context primarily benefits interaction-rich cases. Multi-region training improves generalization in most settings but introduces negative transfer for some source combinations. EnvShip provides a common and reproducible testbed for vessel trajectory forecasting. Code and data are publicly available at https://huggingface.co/datasets/mark000071/envship_v2_datasets.

Figures

Figures reproduced from arXiv: 2606.15240 by the authors.

Figure 1
Figure 1. Benchmark-level statistics of EnvShip-Bench. From left to right, the figure shows the distribution of major vessel [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Representative environment-context cases from EnvShip-Bench. Columns show typical maritime scenarios, including [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Geographic coverage of sampled anchors. Left: spa [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

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Reference graph

Works this paper leans on

46 extracted references · 7 canonical work pages

  1. [1]

    Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese. 2016. Social LSTM: Human Trajectory Prediction in Crowded Spaces. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 961–971

    2016

  2. [2]

    Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom

    Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. 2020. nuScenes: A Multimodal Dataset for Autonomous Driving. InProceedings of 6 the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11621–11631

    2020

  3. [3]

    Millefiori, Nicola Forti, Paolo Braca, and Peter Willett

    Samuele Capobianco, Leonardo M. Millefiori, Nicola Forti, Paolo Braca, and Peter Willett. 2021. Deep Learning Methods for Vessel Trajectory Prediction Based on Recurrent Neural Networks.IEEE Trans. Aerospace Electron. Systems57, 6 (2021), 4329–4346. doi:10.1109/TAES.2021.3096873

    arXiv 2021

  4. [4]

    Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, and James Hays. 2019. Argoverse: 3D Tracking and Forecasting with Rich Maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 8748–8757. doi:10.1109/CVPR.2019.00895

    arXiv 2019

  5. [5]

    X. Chen, J. Ling, Y. Yang, H. Zheng, P. Xiong, O. Postolache, and Y. Xiong. 2020. Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction.Mathematical Problems in Engineering2020 (2020), 7191296. doi:10.1155/2020/7191296

    work page doi:10.1155/2020/7191296 2020

  6. [6]

    Danish Maritime Authority. 2026. AIS Data. https://www.dma.dk/safety-at- sea/navigational-information/ais-data Accessed: 2026-03-31

    2026

  7. [7]

    figshare. 2019. Ushant AIS Dataset. https://figshare.com/articles/dataset/Ushant_ AIS_dataset/8966273 Accessed: 2026-03-31

    arXiv 2019

  8. [8]

    Miao Gao, Maohan Liang, Anmin Zhang, Yingjun Hu, and Jixiang Zhu. 2024. Multi-ship encounter situation graph structure learning for ship collision avoid- ance based on AIS big data with spatio-temporal edge and node attention graph convolutional networks.Ocean Engineering301 (2024), 117605

    2024

Show all 46 references
  1. [9]

    Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi

  2. [10]

    Fangcheng Han, Yang Liu, Hao Tian, Jianchao Li, and Yongjun Tian. 2025. A comprehensive framework incorporating deep learning for analyzing fishing vessel activity using automatic identification system data.ICES Journal of Marine Science82, 2 (2025), fsae166. doi:10.1093/icesj...

    2025 doi

  3. [11]

    I-Lun Huang, Man-Chun Lee, Chung-Yuan Nieh, and Juan-Chen Huang. 2024. Ship Classification Based on AIS Data and Machine Learning Methods.Electronics 13, 1 (2024), 98. doi:10.3390/electronics13010098

    2024 doi

  4. [12]

    Huanhuan Li, Hang Jiao, and Zaili Yang. 2023. Ship trajectory prediction based on machine learning and deep learning: A systematic review and methods analysis. Engineering Applications of Artificial Intelligence126 (2023), 107062

    2023

  5. [13]

    Maohan Liang, Ryan Wen Liu, Yang Zhan, Huanhuan Li, Fenghua Zhu, and Fei-Yue Wang. 2022. Fine-grained vessel traffic flow prediction with a spatio- temporal multigraph convolutional network.IEEE Transactions on Intelligent Transportation Systems23, 12 (2022), 23694–23707

    2022

  6. [14]

    Ryan Wen Liu, Maohan Liang, Jiangtian Nie, Wei Yang Bryan Lim, Yang Zhang, and Mohsen Guizani. 2022. Deep Learning-Powered Vessel Trajectory Pre- diction for Improving Smart Traffic Services in Maritime Internet of Things. IEEE Transactions on Network Science and Engineering9,...

    2022

  7. [15]

    Kun Ma, Qilong Han, and Jingzheng Yao. 2025. Multi-Ship Future Interaction Trajectory Prediction via Pre-Initializer Diffusion Model. InProceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems. 1408–1417

    2025

  8. [16]

    Kun Ma, Qilong Han, Jingzheng Yao, Changmao Wu, and Yuntao Zhang. 2025. DDA: Distillation-Driven Acceleration of the Reverse Diffusion Process for Stochastic Multi-Ship Trajectory Prediction. InICASSP 2025 - 2025 IEEE Inter- national Conference on Acoustics, Speech and Signal ...

    2025

  9. [17]

    MarineCadastre.gov. 2026. AccessAIS. https://marinecadastre.gov/accessais/. Accessed: 2026-03-30

    2026

  10. [18]

    Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, and Christian Claudel

  11. [19]

    Brendan Murray and Lokukaluge P. Perera. 2021. An AIS-Based Deep Learning Framework for Regional Ship Behavior Prediction.Reliability Engineering & System Safety215 (2021), 107819. doi:10.1016/j.ress.2021.107819

    2021

  12. [20]

    Jiquan Ngiam, Vijay Vasudevan, Benjamin Caine, Zheng Zhang, H.-T. L. Chiang, et al. 2022. Scene Transformer: A Unified Architecture for Predicting Future Trajectories of Multiple Agents. InICLR

    2022

  13. [21]

    Duong Nguyen and Ronan Fablet. 2021. TrAISformer: A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS- based Vessel Trajectory Prediction.arXiv preprint arXiv:2109.03958(2021)

    2021 arXiv

  14. [22]

    NOAA Office for Coastal Management and Bureau of Ocean Energy Management

  15. [23]

    Giuseppe Pallotta, Michele Vespe, and Kevin Bryan. 2013. Vessel Pattern Knowl- edge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction.Entropy15, 6 (2013), 2218–2245. doi:10.3390/e15062218

    2013 doi

  16. [24]

    Kikun Park, Sunghyun Sim, and Hyerim Bae. 2021. Vessel estimated time of arrival prediction system based on a path-finding algorithm.Maritime Transport Research2 (2021), 100012. doi:10.1016/j.martra.2021.100012

    2021

  17. [25]

    Huimin Qiang, Zhiyuan Guo, Shiyuan Xie, and Xiaodong Peng. 2023. MSTFormer: Motion Inspired Spatial-temporal Transformer with Dynamic-aware Attention for Long-term Vessel Trajectory Prediction.arXiv preprint arXiv:2303.11540 (2023)

    2023 arXiv

  18. [26]

    Cyril Ray, Richard Dréo, Elena Camossi, Anne-Laure Jousselme, and Clément Iphar. 2019. Heterogeneous Integrated Dataset for Maritime Intelligence, Surveil- lance, and Reconnaissance.Data in Brief25 (2019), 104141. doi:10.1016/j.dib. 2019.104141

    2019

  19. [27]

    Ribeiro, Aline Paes, and Daniel de Oliveira

    Claudio V. Ribeiro, Aline Paes, and Daniel de Oliveira. 2023. AIS-based maritime anomaly traffic detection: A review.Expert Systems with Applications231 (2023), 120561. doi:10.1016/j.eswa.2023.120561

    2023

  20. [28]

    Alexandre Robicquet, Alexandre Alahi, Amir Sadeghian, Bryan Anenberg, John Doherty, Eli Wu, and Silvio Savarese. 2016. Forecasting Social Navigation in Crowded Complex Scenes.arXiv preprint arXiv:1601.00998(2016)

    2016 arXiv

  21. [29]

    H Rong, AP Teixeira, and C Guedes Soares. 2019. Ship trajectory uncertainty prediction based on a Gaussian Process model.Ocean Engineering182 (2019), 499–511

    2019

  22. [30]

    Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, and Marco Pavone. 2020. Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data. InProceedings of the European Conference on Computer Vision (ECCV). doi:10.1007/978-3-030-58523-5_40

    2020 doi

  23. [31]

    Huang Tang, Yong Yin, and Helong Shen. 2022. A model for vessel trajectory prediction based on long short-term memory neural network.Journal of Marine Engineering & Technology21, 3 (2022), 136–145

    2022

  24. [32]

    Technical University of Denmark. 2023. AIS Trajectories from Danish Waters for Abnormal Behavior Detection. https://data.dtu.dk/collections/AIS_Trajectories_ from_Danish_Waters_for_Abnormal_Behavior_Detection/6287841 Accessed: 2026-03-31

    2023

  25. [33]

    Enmei Tu, Guanghao Zhang, Lily Rachmawati, Eshan Rajabally, and Guang- Bin Huang. 2018. Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology.IEEE Transactions on Intelligent Transportation Systems19, 5 (2018), 1559–1582. doi:...

    2018 doi

  26. [34]

    Konrad Wolsing, Linus Roepert, Jan Bauer, and Klaus Wehrle. 2022. Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches.Journal of Marine Science and Engineering10, 1 (2022), 112. doi:10.3390/jmse10010112

    2022 doi

  27. [35]

    Dong Yang, Lingxiao Wu, Shuaian Wang, Haiying Jia, and Kevin X. Li. 2019. How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications.Transport Reviews39, 6 (2019), 755–773. doi:10. 1080/01441647.2019.1649315

    2019

  28. [36]

    Lan You, Siyu Xiao, Qingxi Peng, Christophe Claramunt, Xuewei Han, Zhengyi Guan, and Jiahe Zhang. 2020. St-seq2seq: A spatio-temporal feature-optimized seq2seq model for short-term vessel trajectory prediction.IEEE Access8 (2020), 218565–218574

    2020

  29. [37]

    Ye Yuan, Xinshuo Weng, Yanglan Ou, and Kris M. Kitani. 2021. AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting. InPro- ceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 9813–9823

    2021

  30. [38]

    Zenodo. 2018. Final Dataset of Trajectory Synopses over AIS Kinematic Messages in Brest Area. doi:10.5281/zenodo.2563256

    2018 doi

  31. [39]

    Chengkai Zhang, Junchi Bin, Wells Wang, Xiang Peng, Rui Wang, Richard Halldearn, and Zheng Liu. 2020. AIS data driven general vessel destination prediction: A random forest based approach.Transportation Research Part C: Emerging Technologies118 (2020), 102729. doi:10.1016/j.tr...

    2020

  32. [40]

    Zhang, X

    J. Zhang, X. Ren, H. Li, and Z. Yang. 2022. Incorporation of Deep Kernel Convolution into Density Clustering for Shipping AIS Data Denoising and Reconstruction.Journal of Marine Science and Engineering10, 9 (2022), 1319. doi:10.3390/jmse10091319

    2022 doi

  33. [41]

    Liye Zhang, Qiang Meng, Zhe Xiao, and Xiuju Fu. 2018. A Novel Ship Trajectory Reconstruction Approach Using AIS Data.Ocean Engineering159 (2018), 165–174. doi:10.1016/j.oceaneng.2018.03.085

    2018 doi

  34. [43]

    Xiaocai Zhang, Xiuju Fu, Zhe Xiao, Haiyan Xu, and Zheng Qin. 2022. Vessel Tra- jectory Prediction in Maritime Transportation: Current Approaches and Beyond. IEEE Transactions on Intelligent Transportation Systems23, 11 (2022), 19980–19998. doi:10.1109/TITS.2022.3192574

    2022

  35. [44]

    Zikang Zhou, Li Ye, Jiajun Wang, Kun Wu, and Kejie Lu. 2022. HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction. InCVPR. 7

    2022

  36. [2018]

    InProceedings of the IEEE conference on computer vision and pattern recognition

    Social gan: Socially acceptable trajectories with generative adversarial networks. InProceedings of the IEEE conference on computer vision and pattern recognition. 2255–2264

  37. [2020]

    InProceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 14424–14432

  38. [2023]

    https://coast.noaa.gov/ data/marinecadastre/ais/faq.pdf

    Frequently Asked Questions: AIS Data and Tools. https://coast.noaa.gov/ data/marinecadastre/ais/faq.pdf. Accessed: 2026-03-30

    2026

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