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Detection of maritime traffic anomalies using Satellite-AIS and multisensory satellite imageries: Application to the 2021 Suez Canal obstruction

Published online by Cambridge University Press:  15 August 2022

Ahmed Harun-Al-Rashid
Affiliation:
Marine Security and Safety Research Center, Korea Institute of Ocean Science & Technology, Busan, Korea Department of Aquatic Resource Management, Sylhet Agricultural University, Sylhet, Bangladesh
Chan-Su Yang*
Affiliation:
Marine Security and Safety Research Center, Korea Institute of Ocean Science & Technology, Busan, Korea Department of Convergence Study on the Ocean Science and Technology, Ocean Science and Technology School, Korea Maritime & Ocean University, Busan, Korea Applied Ocean Sciences, University of Science & Technology, Daejeon, Korea
Dae-Woon Shin
Affiliation:
Marine Security and Safety Research Center, Korea Institute of Ocean Science & Technology, Busan, Korea Department of Convergence Study on the Ocean Science and Technology, Ocean Science and Technology School, Korea Maritime & Ocean University, Busan, Korea
*
*Corresponding author. E-mail: [email protected].

Abstract

This study summarises the scenario of maritime traffic anomalies, like the increased congestion and U-turn of ships caused by the ship grounding in the Suez Canal in March 2021. Here, satellite automatic identification system based ship trajectories, and Sentinel-1 and Sentinel-2 images based ship positions are analysed after subdividing the study area into seas, lakes and canals. The results show that the blockage affected the maritime traffic for more than three weeks, waiting ship numbers increased from 5 to 122, and daily one to three ships made a U-turn between 23 and 31 March in the Gulf of Suez. Ship density also increased to more than double in Bitter Lakes with a minimum waiting time of 7 days. Hence, to avoid such prolonged waiting of ships, we propose a warning method based on the sharp speed decrease rate, U-turn and congestion.

Type
Research Article
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Amro, A., Oruc, A., Gkioulos, V. and Katsikas, S. (2022). Navigation data anomaly analysis and detection. Information, 13(3), 104. doi: 10.3390/info13030104.CrossRefGoogle Scholar
Authority, S. C. (2015). Rules of Navigation. Available at: https://www.suezcanal.gov.eg/English/Navigation/pages/rulesofnavigation.aspx. Accessed 10 January 2022.Google Scholar
Authority, S. C. (2019). Suez Canal Traffic Statistics, Annual Report. Available at: https://www.suezcanal.gov.eg/English/Downloads/DownloadsDocLibrary/Navigation%20Reports/Annual%20Reports%E2%80%8B%E2%80%8B%E2%80%8B/2019.pdf. Accessed 8 January 2022.Google Scholar
Bae, J. and Yang, C. S. (2020). A method to suppress false alarms of Sentinel-1 to improve ship detection. Korean Journal of Remote Sensing, 36(4), 535544.Google Scholar
Boztepe, G. (2019). The vessel route pattern extraction and anomaly detection from AIS data. Master's thesis, Middle East Technical University.Google Scholar
Chen, X., Ling, J., Yang, Y., Zheng, H., Xiong, P., Postolache, O. and Xiong, Y. (2020). Ship trajectory reconstruction from AIS sensory data via data quality control and prediction. Mathematical Problems in Engineering, 2020, 19. doi: 10.1155/2020/7191296.Google Scholar
Chen, X., Chen, H., Xu, X., Luo, L. and Biancardo, S. A. (2022). Ship tracking for maritime traffic management via a data quality control supported framework. Multimedia Tools and Applications, 81, 72397252. doi: 10.1007/s11042-022-11951-y.CrossRefGoogle Scholar
Effat, H. A. (2017). Mapping potential wind energy zones in Suez Canal region, using satellite data and spatial multicriteria decision models. Journal of Geoscience and Environment Protection, 5(10), 46.10.4236/gep.2017.510005CrossRefGoogle Scholar
Elsherbiny, K., Tezdogan, T., Kotb, M., Incecik, A. and Day, S. (2019). Experimental analysis of the squat of ships advancing through the new Suez Canal. Ocean Engineering, 178, 331344.10.1016/j.oceaneng.2019.02.078CrossRefGoogle Scholar
Encyclopedia Britannica (n.d.). Suez Canal – History. Available at: https://www.britannica.com/topic/Suez-Canal/History. Accessed 22 October 2021.Google Scholar
Filipiak, D., Strózyna, M., Wecel, K. and Abramowicz, W. (2018). Anomaly Detection in the Maritime Domain: Comparison of Traditional and Big Data Approach. Proceedings of the NATO IST-160-RSM Specialists’ Meeting on Big Data and Artificial Intelligence for Military Decision Making, Bordeaux, France.Google Scholar
Foreign Policy (2021). Available at: https://foreignpolicy.com/2021/11/10/what-the-ever-given-taught-the-world/. Accessed 22 October 2021.Google Scholar
Forti, N., d'Afflisio, E., Braca, P., Millefiori, L. M., Willett, P. and Carniel, S. (2021). Maritime anomaly detection in a real-world scenario: Ever Given grounding in the Suez Canal. IEEE Transactions on Intelligent Transportation Systems, 17. doi: 10.1109/TITS.2021.3123890.Google Scholar
Fu, P., Wang, H., Liu, K., Hu, X. and Zhang, H. (2017). Finding abnormal vessel trajectories using feature learning. IEEE Access, 5, 78987909.10.1109/ACCESS.2017.2698208CrossRefGoogle Scholar
Goodfellow, I. J., Bengio, Y. and Courville, A. C. (2016). Deep Learning. Cambridge: MIT Press.Google Scholar
Griffiths, J. D. (1995). Queueing at the Suez Canal. Journal of the Operational Research Society, 46(11), 12991309.10.1057/jors.1995.179CrossRefGoogle Scholar
Griffiths, J. D. and Hassan, E. M. (1978). Increasing the shipping capacity of the Suez Canal. The Journal of Navigation, 31(2), 219231.10.1017/S0373463300039977CrossRefGoogle Scholar
Handayani, D. O. D., Sediono, W. and Shah, A. (2013). Anomaly Detection in Vessel Tracking Using Support Vector Machines (SVMs). 2013 International Conference on Advanced Computer Science Applications and Technologies. IEEE, 213217.10.1109/ACSAT.2013.49CrossRefGoogle Scholar
Huang, G. (2019). Discovering critical traffic anomalies from GPS trajectories for urban traffic dynamics understanding. Doctoral dissertation, RMIT University.Google Scholar
Jocher, G. (2020). YOLOv5 Documentation. Available at: https://docs.ultralytics.com/.Google Scholar
Keane, K. R. (2017). Detecting Motion Anomalies. Proceedings of the 8th ACM SIGSPATIAL Workshop on GeoStreaming, 2128.10.1145/3148160.3148164CrossRefGoogle Scholar
Khan, I. A. and Rahman, S. (2021). Review and analysis of blockage of Suez Canal region due to giant container ship. Marine Technology Society Journal, 55(5), 3943.10.4031/MTSJ.55.5.5CrossRefGoogle Scholar
Kostianaia, E. A., Kostianoy, A., Lavrova, O. Y. and Soloviev, D. M. (2020). Oil pollution in the northern Red Sea: a threat to the marine environment and tourism development. In: Elbeih, S., Negm, A. and Kostianoy, A. (eds.). Environmental Remote Sensing in Egypt. Cham, Switzerland: Springer, 329362.10.1007/978-3-030-39593-3_12CrossRefGoogle Scholar
Kowalska, K. and Peel, L. (2012). Maritime Anomaly Detection Using Gaussian Process Active Learning. Proceedings of 15th Conference on Information Fusion, 9–12 July 2012, Singapore, Singapore. 11641171.Google Scholar
le Guillarme, N. and Lerouvreur, X. (2013). Unsupervised Extraction of Knowledge From S-AIS Data for Maritime Situational Awareness. Proceedings of the 16th International Conference on Information Fusion. IEEE, 20252032Google Scholar
Liu, B. (2015). Maritime traffic anomaly detection from AIS satellite data in near port regions. Doctoral dissertation, Dalhousie University, Halifax.Google Scholar
March, D., Metcalfe, K., Tintoré, J. and Godley, B. J. (2021). Tracking the global reduction of marine traffic during the COVID-19 pandemic. Nature Communications, 12(1), 112.10.1038/s41467-021-22423-6CrossRefGoogle ScholarPubMed
Martineau, E. and Roy, J. (2011). Maritime Anomaly Detection: Domain Introduction and Review of Selected Literature. Technical Report (No. DRDC-VALCARTIER-TM-2010-460), Defence Research and Development Canada Valcartier, Quebec.Google Scholar
Nguyen, D., Vadaine, R., Hajduch, G., Garello, R. and Fablet, R. (2021). GeoTrackNet- A maritime anomaly detector using probabilistic neural network representation of AIS tracks and a contrario detection. IEEE Transactions on Intelligent Transportation Systems, 113.Google Scholar
Obradović, I., Miličević, M. and Žubrinić, K. (2014). Machine Learning Approaches to Maritime Anomaly Detection. Naše more, 61(5–6), 96101.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013). Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 15(6), 22182245.10.3390/e15062218CrossRefGoogle Scholar
Qu, X., Meng, Q. and Suyi, L. (2011). Ship collision risk assessment for the Singapore Strait. Accident Analysis & Prevention, 43(6), 20302036.10.1016/j.aap.2011.05.022CrossRefGoogle ScholarPubMed
Redmon, J. and Farhadi, A. (2017). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 21-26, July, Honolulu, HI: IEEE.10.1109/CVPR.2017.690CrossRefGoogle Scholar
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition. June 27-July 1, 2016. Las Vegas, NV: IEEE, 779788.10.1109/CVPR.2016.91CrossRefGoogle Scholar
Riveiro, M. (2014). The Importance of Visualization and Interaction in the Anomaly Detection Process. Innovative Approaches of Data Visualization and Visual Analytics, 133150.10.4018/978-1-4666-4309-3.ch007CrossRefGoogle Scholar
Riveiro, M., Falkman, G. and Ziemke, T. (2008). Improving Maritime Anomaly Detection and Situation Awareness Through Interactive Visualization. Proceedings of the 11th International Conference on Information Fusion, FUSION 2008. IEEE, 18.Google Scholar
Riveiro, M., Pallotta, G. and Vespe, M. (2018). Maritime anomaly detection: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5), e1266.Google Scholar
Rong, H., Teixeira, A. P. and Soares, C. G. (2019). Ship trajectory uncertainty prediction based on a Gaussian process model. Ocean Engineering, 182, 499511.10.1016/j.oceaneng.2019.04.024CrossRefGoogle Scholar
Roy, J. (2008). Anomaly Detection in the Maritime Domain. Proceedings of the SPIE- The International Society for Optical Engineering, Vol. 6945.10.1117/12.776230CrossRefGoogle Scholar
Štepec, D., Martinčič, T. and Skočaj, D. (2019). Automated System for Ship Detection From Medium Resolution Satellite Optical Imagery. Oceans 2019 MTS/IEEE Seattle. IEEE, 110.Google Scholar
USA Today (2021). Ever Given refloated and freed! How did they get the ship out of the Suez Canal? Available at: https://www.usatoday.com/in-depth/graphics/2021/03/29/ever-given-refloated-andfreed-how-did-they-get-the-ship-out-of-thesuez-canal/7043678002/ (Accessed 1 January 2022).Google Scholar
Venskus, J., Treigys, P., Bernatavičienė, J., Tamulevičius, G. and Medvedev, V. (2019). Real-time maritime traffic anomaly detection based on sensors and history data embedding. Sensors, 19(17), 3782.10.3390/s19173782CrossRefGoogle ScholarPubMed
Wang, Y., Wang, C. and Zhang, H. (2018). Combining a single shot multibox detector with transfer learning for ship detection using Sentinel-1 SAR images. Remote Sensing Letters, 9(8), 780788.10.1080/2150704X.2018.1475770CrossRefGoogle Scholar
Xiaye, T. (2015). Research on liner shipping schedule recovery. MS dissertation, World Maritime University).Google Scholar
Yang, C. S., Park, J. H. and Rashid, A. H. A. (2018). An improved method of land masking for synthetic aperture radar-based ship detection. The Journal of Navigation, 71(4), 788804.10.1017/S037346331800005XCrossRefGoogle Scholar
Zhou, J., Zhao, Y. and Liang, J. (2021). Multiobjective route selection based on LASSO regression: When will the Suez Canal lose its importance? Mathematical Problems in Engineering, 2021, 118. doi:10.1155/2021/6613332.Google Scholar