Open Access
| Issue |
E3S Web Conf.
Volume 687, 2026
The 2nd International Conference on Applied Sciences and Smart Technologies (InCASST 2025)
|
|
|---|---|---|
| Article Number | 02013 | |
| Number of page(s) | 9 | |
| Section | Green Technologies & Digital Society | |
| DOI | https://doi.org/10.1051/e3sconf/202668702013 | |
| Published online | 15 January 2026 | |
- UNCTAD, Review of Maritime Transport 2023. United Nations Conference on Trade and Development, 2023. [Online]. Available: https://unctad.org/topic/transport-and-trade-logistics/review-of-maritime-transport [Google Scholar]
- J. Ribeiro, P. Ferreira, and J. Cunha, “AIS-based maritime anomaly traffic detection: A review,” Expert Syst Appl, vol. 222, p. 119803, 2023, doi: 10.1016/j.eswa.2023.119803. [Google Scholar]
- A. Stach, M. Steinmetz, and L. Sommer, “Maritime anomaly detection for vessel traffic services,” J Mar Sci Eng, vol. 11, no. 6, p. 1174, 2023, doi: 10.3390/jmse11061174. [Google Scholar]
- Y. Zhao, B. Xu, and W. Zhang, “Ship detection with deep learning in optical remote sensing images: A review,” Remote Sens (Basel), vol. 16, no. 7, p. 1145, 2024, doi: 10.3390/rs16071145. [Google Scholar]
- A. Gupta, R. Patel, and J. Wang, “Ship detection using ensemble deep learning techniques,” Sci Rep, vol. 14, p. 80239, 2024, doi: 10.1038/s41598-024-80239-y. [Google Scholar]
- N. Evmides, D. Tsouros, and N. Paterakis, “Deep recurrent architectures for AIS trajectory prediction,” J Mar Sci Eng, vol. 13, no. 8, p. 1439, 2025, doi: 10.3390/jmse13081439. [Google Scholar]
- J. Li, Z. Liu, and Y. Wang, “Deep learning for SAR ship detection: Past, present and future,” Remote Sens (Basel), vol. 14, no. 11, p. 2712, 2022, doi: 10.3390/rs14112712. [Google Scholar]
- J. Mäyrä, A. Lehikoinen, and V. Matikka, “Mapping recreational marine traffic with Sentinel-2: Constraints and opportunities,” Remote Sens Environ, vol. 312, p. 113287, 2025, doi: 10.1016/j.rse.2025.113287. [Google Scholar]
- M. Riveiro, G. Pallotta, and M. Vespe, “Maritime anomaly detection: A review,” Wiley Interdiscip Rev Data Min Knowl Discov, vol. 8, no. 5, p. e1266, 2018, doi: 10.1002/widm.1266. [Google Scholar]
- K. Wolsing, H. Nguyen, and H. Frey, “Anomaly detection in maritime AIS tracks: A review of recent approaches,” J Mar Sci Eng, vol. 10, no. 1, p. 112, 2022, doi: 10.3390/jmse10010112. [Google Scholar]
- S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of the 32nd International Conference on Machine Learning (ICML), PMLR, 2015, pp. 448–456. [Online]. Available: https://proceedings.mlr.press/v37/ioffe15.html [Google Scholar]
- Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539. [CrossRef] [Google Scholar]
- S. Patel, R. Mishra, and S. Roy, “Deep learning-based automatic detection of ships,” Sensors, vol. 22, no. 15, p. 5223, 2022, doi: 10.3390/s22155223. [Google Scholar]
- X. Ren, Q. Gao, and Y. Chen, “Multi-feature fusion with CNN for ship classification,” Applied Sciences, vol. 9, no. 20, p. 4209, 2019, doi: 10.3390/app9204209. [Google Scholar]
- M. Syed, S. Ahmed, and A. Ali, “CNN-LSTM for vessel track prediction and anomaly detection,” J Mar Sci Eng, vol. 11, no. 4, p. 632, 2023, doi: 10.3390/jmse11040632. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

