research-article

A Digital Twin for Maritime Situational Awareness

Authors:

Alexandros Troupiotis-Kapeliaris,

Giannis Spiliopoulos,

Georgios Grigoropoulos,

Dimitris ZissisAuthors Info & Claims

BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies

Article No.: 26, Pages 1 - 2

https://doi.org/10.1145/3632366.3632378

Published: 03 April 2024 Publication History

Get Access

Abstract

Monitoring vessel traffic on a global scale is a complex and challenging task. The large number of moving vessels and the complexity of monitoring their position and forecasting their route in real-time require novel, advanced and highly scalable big-data mechanisms. In this work a digital twin for constant maritime situational awareness on a global scale is presented. The described multi-layered system is able to visualize maritime traffic in real-time, based on data from the Automatic Identification System (AIS), while also providing forecasts of future movement based on machine learning and deep learning techniques. The system is validated using real streaming AIS data from around the globe to demonstrate its performance, scalability and parallelization efficiency.

References

[1]

Eva Chondrodima, Nikos Pelekis, Aggelos Pikrakis, and Yannis Theodoridis. 2023. An Efficient LSTM Neural Network-Based Framework for Vessel Location Forecasting. IEEE Transactions on Intelligent Transportation Systems (2023).

Digital Library

Google Scholar

[2]

Mordechai Haklay and Patrick Weber. 2008. OpenStreetMap: User-Generated Street Maps. IEEE Pervasive Computing 7, 4 (2008), 12--18.

Digital Library

Google Scholar

[3]

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 Intelligence 126 (2023), 107062.

Digital Library

Google Scholar

[4]

International Maritime Organization. [n. d.]. SOLAS Chapter V, Regulation 19.2. https://www.navcen.uscg.gov/sites/default/files/pdf/AIS/SOLAS.V.19.2.1-5.pdf [Accessed 16-10-2023].

Google Scholar

[5]

Alexandros Troupiotis-Kapeliaris, Nicolas Zygouras, Manolis Kaliorakis, Spiros Mouzakitis, Giannis Tsapelas, Alexander Artikis, Eva Chondrodima, Yannis Theodoridis, and Dimitris Zissis. 2022. Data Driven Digital Twins for the Maritime Domain. In Technology and Science for the Ships of the Future. IOS Press, 744--751.

Google Scholar

[6]

Dimitris Zissis, Konstantinos Chatzikokolakis, Giannis Spiliopoulos, and Marios Vodas. 2020. A distributed spatial method for modeling maritime routes. IEEE Access 8 (2020), 47556--47568.

Crossref

Google Scholar

Cited By

View all

Troupiotis-Kapeliaris AKastrisios CZissis D(2025)Vessel Trajectory Data Mining: A ReviewIEEE Access10.1109/ACCESS.2025.352595213(4827-4856)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3525952
Homayouni SPinho de Sousa JMoreira Marques C(2024)Unlocking the potential of digital twins to achieve sustainability in seaports: the state of practice and future outlookWMU Journal of Maritime Affairs10.1007/s13437-024-00349-2Online publication date: 22-Aug-2024
https://doi.org/10.1007/s13437-024-00349-2

Index Terms

A Digital Twin for Maritime Situational Awareness
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

Recommendations

National and international maritime situational awareness model examples and the effects of North Stream Pipelines sabotage
Abstract
While many countries and international organizations with maritime security interests and rights at sea have developed new security strategies or policies in the wake of the 9/11 terrorist attacks in the United States of America, they ...
Visualising maritime vessel open data for better situational awareness in ice conditions
Mindtrek '18: Proceedings of the 22nd International Academic Mindtrek Conference

Situational awareness of maritime vessels in ice conditions is important for the operation of supply chains. In the artic sea areas, the ice conditions pose a major challenge for maritime vessels getting stuck in the ice and being significantly delayed ...
A document-based data model for large scale computational maritime situational awareness
BIG DATA '15: Proceedings of the 2015 IEEE International Conference on Big Data (Big Data)

Computational Maritime Situational Awareness (MSA) supports the maritime industry, governments, and international organizations with machine learning and big data techniques for analyzing vessel traffic data available through the Automatic ...

Comments

Information & Contributors

Information

Published In

BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies

December 2023

187 pages

ISBN:9798400704734

DOI:10.1145/3632366

Co-chairs:
Omer Rana,
Massimo Villari,
Program Co-chairs:
Geoffrey Fox,
Maria Fazio

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 April 2024

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

European Union?s Horizon 2020

Conference

BDCAT '23

Sponsor:

SIGARCH

BDCAT '23: IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies

December 4 - 7, 2023

Taormina (Messina), Italy

Acceptance Rates

Overall Acceptance Rate 27 of 93 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
69
Total Downloads

Downloads (Last 12 months)69
Downloads (Last 6 weeks)7

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Troupiotis-Kapeliaris AKastrisios CZissis D(2025)Vessel Trajectory Data Mining: A ReviewIEEE Access10.1109/ACCESS.2025.352595213(4827-4856)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3525952
Homayouni SPinho de Sousa JMoreira Marques C(2024)Unlocking the potential of digital twins to achieve sustainability in seaports: the state of practice and future outlookWMU Journal of Maritime Affairs10.1007/s13437-024-00349-2Online publication date: 22-Aug-2024
https://doi.org/10.1007/s13437-024-00349-2

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

National and international maritime situational awareness model examples and the effects of North Stream Pipelines sabotage

Visualising maritime vessel open data for better situational awareness in ice conditions

A document-based data model for large scale computational maritime situational awareness

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations