Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Oceanic Plastic Waste Detection with AI and Remote Sensing is a 3-day advanced course focused on using satellite imagery, machine learning, and predictive modeling to identify, track, and optimize cleanup strategies for marine plastic pollution. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Oceanic Plastic Waste Detection with AI and Remote Sensing is a 3-day advanced course focused on using satellite imagery, machine learning, and predictive modeling to identify, track, and optimize cleanup strategies for marine plastic pollution.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Detection from Orbit
Analyze satellite imagery using Sentinel-2 SWIR bands to detect floating plastic waste. • Apply multi-scale detection techniques using satellite and UAV imagery with Super-Resolution GANs. • Implement debris segmentation using U-Net and DeepLabV3+.
Movement & Drift Prediction
Model ocean transport using Eulerian and Lagrangian approaches. • Develop RNN-based drift forecasting models for marine debris. • Predict debris accumulation zones using ConvLSTM heatmap prediction.
Cleanup Strategy Optimization
Apply reinforcement learning for autonomous vessel routing. • Implement YOLO-based waste classification. • Coordinate drone swarms for debris scouting.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Sentinel-2 |
| Covered Tool / Platform | Super-Resolution GANs |
| Covered Tool / Platform | U-Net |
| Covered Tool / Platform | DeepLabV3+ |
| Covered Tool / Platform | Colab |
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