ML for Ocean Health: Monitoring Marine Ecosystems with AI explores how artificial intelligence and machine learning can transform ocean monitoring and conservation.Participants will learn to analyze satellite, acoustic, and underwater imaging data to track biodiversity, pollution, and ecosystem changes.
Aim
To equip participants with practical AI and machine learning skills for monitoring and protecting marine ecosystems.
Workshop Objectives
Introduce core ML concepts for ocean health monitoring.
Build skills to analyze satellite, acoustic, and biodiversity datasets.
Apply AI to detect ecosystem shifts, pollution patterns, and species distribution.
Develop predictive models to support conservation and decision-making.
Workshop Structure
Day 1: Bio-Indicators & Computer Vision
Coral Health Classification: Hybrid CNN-SVM models for
Healthy / Stressed / Bleached corals (radiometric + water-column correction).
Automated Species ID & Abundance: YOLOv10 for real-time fish counting and species identification from ROV footage.
Acoustic Soundscape Monitoring: Spectrogram-based deep learning to estimate biodiversity via Biophony vs Anthropophony.
🚀 Colab Hands-on:[Coral-Health-Classifier] —
Build a pipeline to detect bleaching in high-resolution reef imagery using the CoralNet open dataset.
Day 2: Pollution Tracking & Habitat Stress
Multi-Sensor Pollution Detection: SAR + optical fusion to identify oil spills and chemical runoff plumes near industrial zones.
Predictive Algal Bloom Modeling: LSTM forecasting of HABs using SST, chlorophyll-a, and nutrient runoff indicators.
Mangrove & Seagrass “Blue Carbon” Mapping: AI segmentation of coastal carbon sinks to estimate sequestration for environmental credits.
🚀 Colab Hands-on:[HAB-Early-Warning] —
Create a time-series model to predict chlorophyll-a spikes using Copernicus Marine satellite data.
Day 3: Conservation Strategy & Policy AI
RL for Marine Protected Areas (MPAs): Simulate and optimize protected-zone boundaries to maximize species recovery while minimizing economic impact.
Illegal Fishing Detection: AIS trajectory analysis to flag transshipment and suspicious mid-ocean behavior.
XAI for Environmental Policy: SHAP heatmaps to explain why a coastal area is prioritized for restoration.
🚀 Colab Hands-on:[Vessel-Behavior-AI] —
Implement Isolation Forest anomaly detection to identify suspicious loitering patterns in vessel GPS data.
Who Should Enrol?
Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
02/18/2026 IST 4 PM
Workshop Dates
02/18/2026 – 02/20/2026 IST 5: 30PM
Workshop Outcomes
Understand AI applications in marine ecosystem monitoring.
Analyze oceanographic and biodiversity datasets using ML tools.
Build basic predictive models for environmental change detection.
Apply data-driven strategies to support sustainable ocean management.
Fee Structure
Student
₹1999 | $60
Ph.D. Scholar / Researcher
₹2999 | $70
Academician / Faculty
₹3999 | $80
Industry Professional
₹5999 | $100
What You’ll Gain
Live & recorded sessions
e-Certificate upon completion
Post-workshop query support
Hands-on learning experience
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Publication Opportunity
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