GSIN Geospatial Information Networking Techniques for Maritime Awareness Services

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The automatic identification system (AIS) is essential to maritime traffic situation awareness. AIS tracks vessels' movements and is invaluable for detecting and monitoring suspicious activity. However, AIS can be switched off accidentally or deliberately to hide suspicious activities. These ships put marine traffic safety at risk. 

Detecting dark vessels is more than having no AIS messages or observing when an AIS message is not received beyond a specific time interval. The reason is that AIS message transmission is affected by many factors, such as distance from receivers or base stations, weather and environmental conditions, and proximity to ports. Furthermore, AIS data lack information about the vessels' real-time environment and their reaction to adverse environments. 

This project’s objective is to build a set of deep learning models supported by the integration of a diverse set of data sources which will allow maritime safety and security stakeholders to accurately:

  1. Detect dark ships

  2. Monitor the activities of the located dark ship

  3. Locate the identified dark vessel

  4. Support Search and Rescue activities


The key challenges for the provision of the Maritime Awareness Service System are:

  1. Acquisition of suitable amounts of satellite data for machine learning model training purposes. The usage of multiple satellite vendors with different capabilities and external interfaces is required and post processing to have a baseline standard of imagery for training is required within the system.

  2. Accuracy and availability of RF data. Since RF is a new and emerging technology additional research into its capabilities and selection of RF providers is required within the project.

  3. Availability of ground truth in relation to dark activity of vessels. By nature the identification of dark activity has historical being difficult and is required for model training. Simulation of dark vessel activities will be used to increase the data set for ML modelling purposes. 

  4. Availability of historical archive of global AIS data for ML training.


The core product benefits and value relative to existing competing systems are:

  1. Enhance the ability for reliable ship identification when AIS systems are turned off or disabled. The target is to detect at least 50% of dark vessels activities in selected regions of interest.

  2. Enhance reliable ship location when AIS tracking not available. The target is to predict the track and location of the vessel correctly at least 80% of the time.

  3. Enhance reliable ship identification. The target is to detect and identify at least 50% of dark ships activities in region of interest.

  4. Compared to AIS-only, the target improvement in the detection is the one order of magnitude.


The Maritime Awareness Service main feature is to improve the current state-of-the-art in the domain of vessel detection and identification by fusing the data from multiple sources and through the implementation of ML models provide new ways of processing, fusing and analysing multiple data streams. This approach improves the quality of vessel detections and analytical capabilities for the maritime domain.

The key end user features are:

  • Applying the data mining and deep learning techniques to increase the accuracy of vessel detection

  • Applying the data mining and deep learning techniques to increase the accuracy of vessel identification

  • Forecasting the future tracks of the vessels and utilizing the forecasts to enhance the tracking of dark activity of non-cooperative vessels.

  • Support both insurance compliance officers, marine security and search and rescue end users through Maritime Awareness Service SAAS web application.

System Architecture

The following diagram provides a high-level overview of the three-layer architecture consisting of:

  1. Presentation Layer

  2. Application Layer

  3. Data Layer

which is the basis for the Maritime Awareness Service demonstrator system.

Figure 12: Three-layer service architecture
Figure 12: Three-layer service architecture

The key logical components in the three-layer architecture are:

  1. Time Series Data Ingestion – Component which ingests data sources.

  2. EO/SAR/Image Ingestion – Component to ingest image sources.

  3. Message Data Ingestion – Component to ingest VDE/DSC message data.

  4. Service Data Storage – Local storage of all ingested data sources from multiple sources.

  5. Time series data processing

  6. Data Fusion and Processing Unit – Providing of data fusion functionality for all ingested data sources.

  7. ML WorkFlow Engine – Provision of all ML workflow and modelling services.

  8. Geospacial Information Network – Core component for provision of application functionality and services.

  9. End User GUI – Component for delivery of web application front end and all system services.


The project is scheduled to be completed over an 18 month timeframe with the KO taking place on the 13th March 2023. 

The primary milestones and phases for the project are as follows:

  1. KO – March 2023

  2. Preliminary Design Review (PDR) – September 2023

  3. Acceptance Review (AR) – March 2024

  4. Final Review    (FR) – August 2024

  5. Final Presentation (FP) – September 2024

Current status

The project was successfully kicked off on the 13th March 2023 with the initial focus on selection of the high level use cases which form the basis for the system services. 

To support the use case definition and refinement from the initially proposed selection within the project proposal, two workshops were held. The first workshop held at the Lloyds building London attended by key marine underwriters and compliance experts focused on the needs for dark vessel and identifying potential illegal activities. The second workshop was held in Maynooth University Ireland was attended by Naval Services, Coast Guards, Custom and offshore energy experts stakeholders. 

The workshops clearly provided the core set of use cases and user requirements as the baseline for the Maritime Awareness Service. Based on end user needs, technical prototyping and the services technical baseline have been defined. 

The activities about to start are to take the technical baselines and user requirements and defined the detailed system requirements and system architecture for the Maritime Awareness Service to be presented at the PDR milestone.

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