Earth Observation Techniques for Fisheries Management - 2nd Edition

Resumen

A 3-week, fully online, asynchronous-first training programme preceded by a mandatory 1-week Pre-Course phase (Week 0) that provides an entry via a self-assessment quiz. The course equips fisheries officers, MCS analysts, marine scientists and data analysts with Python-based workflows for AIS data engineering, rule-based IUU behaviour detection, and multi-sensor Earth Observation (EO) fusion, cross-referencing Sentinel-1 SAR-derived vessel detections against AIS to identify vessels that operate without transponders ("dark fleet").Case studies span three regions (Gulf of Guinea (West Africa), Mozambique Channel (East Africa) and the Eastern Tropical Pacific) so participants from different operational contexts see at least one case anchored near their own waters.

Learning outcomes: 

By the end of the course, participants will be able to:

• Set up a reproducible Python (Miniconda) environment for geospatial and remote-sensing analysis (verified at Week 0).
• Ingest, clean and reconstruct AIS vessel trajectories using Pandas, and GeoPandas.
• Compute vessel kinematics and apply rule-based algorithms to detect foreign vessel incursion, MPA breach, and co-located vessel behaviours (bunkering, transshipment).
• Execute spatial queries against Marine Protected Areas and EEZ boundaries using GeoPandas.
• Interpret and analyse pre-computed Sentinel-1 SAR vessel detections, understanding the pipeline that produced them.
• Perform multi-sensor fusion (SAR + AIS + VIIRS Boat Detection) to identify vessels not visible in AIS (the "dark fleet").
• Produce MCS-usable outputs (alert tables, one-page briefs) suitable for national fisheries authority reporting and PSMA pre-arrival risk profiling.

Course content: 

Week 0 — Pre-Course (Mandatory)

• Welcome video, environment setup guide, Python refresher notebook, geospatial primer, operational context reading.
• Gated self-assessment quiz (20 questions, 70% threshold, 3 attempts). Participants who do not pass are offered graceful deferral to Edition 3.

Week 1 — AIS Data Engineering & Geospatial Foundations

• 1.1 Python & Jupyter environment (refresher; Week 0 has already verified setup).
• 1.2 AIS architecture, message types, data quality
• 1.3 Data cleaning, trajectory reconstruction with MovingPandas, visualisation.
• Lab 1 — Building the AIS Pipeline (Case Study 1: Gulf of Guinea foreign incursion context).

Week 2 — Behavioural Analysis & IUU Detection

• 2.1 Vessel kinematics, state classification, AIS gap detection and spoofing follow-up.
• 2.2 Foreign vessel incursion detection (primary technique); co-location detection for bunkering, rendezvous, transshipment (secondary technique).
• 2.3 Integrating fisheries boundaries, composite risk scoring, MCS-usable output format.
• 2.4 (Friday) Ocean remote sensing preview - short lightweight piece before Week 3.
• Lab 2 - Automated IUU Alert System (Case Study 1 continued).

Week 3 — Multi-Sensor Fusion & the Dark Fleet

• 3.1 Working with SAR-derived vessel detections
• 3.2 multi-sensor fusion methodology (SAR–AIS matching + VIIRS Boat Detection cross-check); worked demo — Case Study 3 (Eastern Tropical Pacific, flag-state-anonymised).
• Closing Session — Capstone live presentations; MCS integration; regional data governance (FCWC, WA-MCSN, SWIOFC/IOTC, Yaoundé Architecture); PSMA framing.
• Lab 3 (Capstone) — Multi-Sensor Fusion Case Study, Case Study 2 (Mozambique Channel cross-border poaching).

Target audience and prerequisite:

Fisheries officers, MCS analysts and staff of national fisheries authorities, marine scientists, and data analysts working in ocean or maritime domains. Priority for applicants from West and Central African coastal states (Ghana, Nigeria, Senegal, Sierra Leone, Liberia, Côte d'Ivoire, Togo, Cameroon, Gabon, and neighbouring states under the Yaoundé Architecture).

Edition 2 also welcomes applicants from East Africa and Asia-Pacific, reflecting the three-region case study anchoring.

Prerequisites:

• Basic computer literacy — installing software, working with files and folders.
• PRIOR PYTHON EXPERIENCE is now required — equivalent to approximately 10 hours of self-study (e.g. Python for Everybody or an introductory MOOC). Complete beginners will not pass the Week 0 gating quiz and will be offered graceful deferral to Edition 3.
• Ability to install software on a personal computer to meet the hardware spec (8 GB RAM, 20 GB free disk).

Language of instruction: English

Instructors:

• Dr Kwame A. Agyekum - lecturer at the University of Ghana
• Daniel Quarshie -Earth Observation Officer, Regional Marine Centre, University of Ghana

Synchronous sessions:

Weeks 1–3 include two synchronous touchpoints per week plus a Closing Session, delivered via Zoom / MS Teams. All sessions recorded and posted to Moodle within 24 hours. Attendance strongly encouraged but not mandatory.

Wednesdays — Live Clinic (1 hr) for debugging, environment issues and conceptual Q&A.

Fridays — Wrap-Up Review (30 min) covering common lab errors and previewing the next week.

Final Friday of Week 3 — Closing Session (1 hr): 3-minute live capstone presentations, MCS integration, PSMA framing, course evaluation.

Week 0 has NO synchronous sessions but offers bookable 20-minute office-hours slots for participants needing setup help or gating-quiz retake support.

Duration:

Total participant workload: approximately 50–55 hours across 4 weeks.

• Week 0 (pre-course): ~5–8 hours — fully asynchronous. Welcome video (~10 min), setup guide, Python refresher notebook (~2 hr), geospatial primer, gating quiz (~30 min).
• Weeks 1–3 (Delivery): ~15 hours/week.
• Asynchronous: ~13 hours/week (video lectures ~90 min, reading ~60 min, lab notebooks ~10 hr).
• Synchronous: ~1.5 hours/week (1-hr Live Clinic + 30-min Wrap-Up), plus one 1-hr Closing Session in Week 3.

learner assessment:

Assessment is competency-based, weighted across three graded lab notebooks, a capstone presentation and forum participation. Week 0 pre-course quiz is pass/fail and does NOT contribute to the graded weighting since it is a gate, not an assessment.

Week 0 Gating Quiz - pass/fail gate (70% threshold). Failure triggers graceful deferral offer.

Week 1 Lab - 20%

Week 2 Lab - 25%

Week 3 Lab / Capstone - 30%

Capstone Presentation - 15%

Participation (forums, live clinics, Error Bank contributions) - 10%

An OTGA certificate will be issued through the OTGA platform on successful completion. 

Passing threshold: 70% overall weighted score

Technology requirements

• Laptop or desktop with minimum 8 GB RAM and 20 GB free disk space (Windows, macOS or Ubuntu).
• Stable internet sufficient for Moodle, Zoom/MS Teams, and downloading pre-packaged case study data (typical size < 500 MB per week).
• Modern browser (Chrome or Firefox recommended).
• Basic computer literacy PLUS prior Python experience, verified via the Week 0 gating quiz

Pre-requisites:

there will be a MANDATORY Week 0 Pre-Course phase

• Week 0 opens 1 week before Week 1 (indicative: 5 October 2026).
• Content: welcome video, environment setup guide (Miniconda installation script tested on Windows, macOS and Ubuntu), Python refresher notebook, geospatial primer, operational context reading.
• Gated 20-question self-assessment quiz on Moodle (70% threshold, 3 attempts). Covers Python basics, pandas, matplotlib, environment imports and geospatial primer concepts.
• Participants who do not pass by end of Week 0 are offered a guaranteed seat in Edition 3 (2027), with a graceful deferral protocol rather than being set up to fail in Week 1.

All software required is free and open source. No institutional licenses required.

Application and selection of participants:

A limited number of seats (approximately 30) are available. Please fill out the online application form available in this Link 

Applications open 18 september 2026 . The deadline to submit the application is 27 September 2026 (23:59 CEST: Central European Summer Time). When selected you will be notified immediately to start the week after. 

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 Daniel Quarshie (danielquarshie02@gmail.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.