UNESCO-IOCAFRICA/OTGA/GMES & Africa (MarCNoWA): Earth Observation for Enhanced Fisheries Monitoring, Control, and Surveillance

Résumé

This training is designed to equip participants with the skills and knowledge required to utilise Automatic Identification System (AIS) data to strengthen monitoring, control, and surveillance (MCS) efforts in the fisheries sector. AIS, originally developed as a collision avoidance tool for maritime transport, has evolved into a valuable resource for fisheries surveillance and enforcement.

Through this training, participants will explore how AIS data can be used to track and monitor fishing vessel activity, identify potential illegal, unreported, and unregulated (IUU) fishing, and support compliance with fisheries management regulations. AIS provides critical information such as vessel speed, course overground, and positional data (latitude and longitude), which are essential for understanding vessel behaviour and operational patterns.

The course also highlights the mandatory requirement for industrial fishing vessels to install electronic monitoring systems, including AIS, to ensure transparency and accountability. Participants will gain practical experience in analysing AIS data, interpreting vessel tracks, and integrating this information into fisheries management strategies, ultimately contributing to sustainable fisheries and improved maritime security.

Learning outcomes

• Demonstrate how to process and visualise AIS data using relevant software tools and techniques.
• Identify and describe the key components and data attributes of AIS, including speed, course over ground, and positional information.
• Interpret AIS data to detect and analyse vessel activity patterns, including identifying potential IUU fishing activities.

Course content

• Introduction to Python
  • Overview of Python programming language
  • Installing Python and setting up the environment
  • Key Python concepts: variables, data types, loops, and functions
• Introduction to Jupyter Notebook
  • Setting up Jupyter Notebook for data analysis
  • Navigating the Jupyter interface
  • Writing and executing Python code in Jupyter Notebook
• Introduction to Relevant Python Libraries
  • Pandas: Data manipulation and analysis
  • NumPy: Numerical computations
  • Matplotlib/Plotly: Data visualisation
  • Geopandas: Geospatial data analysis
• Introduction to AIS Data
  • Overview of the Automatic Identification System (AIS)
  • Key attributes of AIS data
  • Importance of AIS data for monitoring, control, and surveillance
  • Sources of AIS data and formats
• Processing AIS Data with Python
  • Reading AIS data files using Pandas
  • Cleaning and preparing AIS data for analysis
  • Handling missing or erroneous data in AIS datasets
• Analysing AIS Data
  • Visualising vessel tracks using Python libraries
  • Calculating vessel speeds and identifying anomalies
  • Detecting potential IUU fishing activities based on AIS patterns
• Practical Applications of AIS Data
  • Integrating AIS data into fisheries management systems
  • Case studies: Detecting transshipment using AIS data

Target audience and prerequisites

This course is designed for researchers, fisheries officers, students, and Early Career Ocean Professionals (ECOPs) actively engaged in fisheries management, maritime security, and coastal conservation in Africa.

Language: English

Instructors

Dr. Kwame Adu Agyekum (Department of Marine and Fisheries Sciences, University of Ghana)

Mr. Ignatius Kweku Williams (GMES and Africa project, University of Ghana)

Mr. Daniel Quarshie (GMES and Africa project, University of Ghana)

Course format and duration

This course will be held online and includes synchronous sessions, for which attendance is mandatory. These sessions are scheduled for:  9 – 13 July 2025 11:00 -13:00 UTC / GMT Daily.

The estimated duration of this course is 20 hours (10 hours of synchronous classes and 10 hours of asynchronous activities). 

Organizers

RTC Ghana

MarCNoWA: GMES & Africa Project

IOCAFRICA - Intergovernmental Oceanographic Commission of UNESCO Sub-Commission for Africa and the Adjacent Island States

Learner assessment

Assignments (e.g, written and data exercises) and multiple-choice quizzes.

Certificate

OTGA Certificate will be awarded to the participants upon completion of assignments (pass mark: 70%) and 60% attendance at the synchronous sessions.

Technology requirements, computer skills, and pre-requisites

• Computer with Windows or Mac OS
• Latest version of either Chrome, Edge, Firefox or Safari web browsers
• JavaScript and cookies enabled
• Broadband internet access (minimum bandwidth of 0.5 Mbps (Receive and Send)
• Speakers or headphones, and microphone

You should be proficient in the following:

For this course, participants will be required to install Python (list of packages required will be made available), and are expected to be familiar with data software, such as Python, QGIS, Google Earth, and Microsoft Excel. 

Application and selection of participants.

A limited number of seats (approximately 18) are available. Please fill out the online application form available in this LINK  

The applications start on 10 June 2025. The deadline to submit the application is 25 June 2025 (23:59 CEST: Central European Summer Time).

Participants will be selected based on the fit to the target audience, motivation statement, and professional background/qualification. 

Costs: This is an online course with no tuition fees.

Contacts

For any questions, please contact OTGA Secretariat (ioc.training@unesco.org) or the course coordinator Ignatius Kweku Williams (kwilliams@ugedugh.onmicrosoft.com) always using the name of the course as e-mail subject.

Feedback survey

At the end of the course, you will be asked to fill out a feedback survey. This information will be used to improve future courses.

In the event of cancellation of the course by the OTGA or its affiliates, we will provide notification of cancellation at least 7 days prior to the course date. In the event of cancellation by the attendee, we should receive notification of cancellation at least 7 days prior to the course date.