AI in Plastic Lifecycle Analysis: Detection, Tracking, and Mitigation

• Use computer vision and geospatial AI to detect and map plastic pollution hotspots from satellite and UAV imagery.
• Implement machine learning models for classifying microplastics based on spectral data (e.g., Raman spectroscopy).
• Model plastic transport in ecosystems, incorporating environmental variables like wind, tides, and river currents.
• Develop temporal forecasting models to predict seasonal pollution trends using AI-based time-series analysis.
• Understand the role of AI in the circular economy and apply predictive analytics to waste management and recycling optimization.

Day 1 AI for Plastic Pollution Analytics

• Introduction to Plastic Pollutants, Master the use of Computer Vision and Geospatial AI to identify plastic accumulation hotspots from the sky to the shore
• Remote Sensing (Satellites & Drones): AI models, Computer Vision (Shipborne Cameras): AI systems
• Satellite Intelligence: Leveraging Sentinel-2 and UAV imagery for automated plastic debris mapping
• Neural Architectures: Implementing YOLO and CNN frameworks for real-time object detection in marine environments
• The MDPI Advantage: Synthesizing open-access research data to pinpoint urban vs. industrial leakage points
• Micro plastic Analysis: 3D AI-powered systems (e.g., MP3D),
• Hands-on Lab: “The Polymer Classifier” – Build a Python-based classifier in Google Colab to identify microplastic types (PE, PET, PP) using Raman Spectroscopy datasets

Day 2 Dynamics of Micro plastics & Transport Pathways

• Move from observation to action by forecasting future pollution trends and optimizing mitigation strategies
• Temporal Forecasting: Using Recurrent Neural Networks (RNNs) and LSTMs to predict seasonal pollution surges
• Transport Modeling: Utilizing AI-driven Lagrangian particle tracking to simulate plastic flow in riverine system
• Microplastic Fingerprinting: Using Random Forests and Support Vector Machines (SVM) to classify polymers via spectroscopy data
• Environmental Correlation: Feature engineering techniques to link wind, tide, and weather patterns to plastic drift
• Hands-on Lab: “The Drift Navigator” – Develop a Random Forest regression model in Google Colab to correlate environmental variables (currents, wind, topography) with plastic drift trajectories

• Pathway Prediction & Modelling: Employing deep learning, reinforcement learning,
• Microplastic Analytics: Developing tools that analyze oceanic factor
• Industrial Sorting & Recycling, Data-Driven Environmental Solutions, Geospatial Analysis
• Digital Twins: AI’s role in creating virtual models of waste-to-energy pathways and sorting facility optimization
• Policy & ESG Impact: Generating predictive risk maps to guide government intervention and corporate sustainability goals
• Hands-on Lab: “The Trend Forecaster” – Develop an LSTM time-series model in Google Colab to predict plastic accumulation rates based on historical environmental variables

• Learned how to use AI and computer vision for detecting plastic pollution from satellite and UAV imagery.
• Built and deployed machine learning models for classifying microplastics using spectral data.
• Developed transport models to simulate plastic flow and drift in ecosystems.
• Created temporal forecasting models to predict future plastic pollution trends.
• Applied AI for optimizing circular economy strategies in waste management and plastic recycling.

• Environmental scientists, policy makers, and sustainability experts.
• Researchers and data scientists in the fields of marine science, pollution, and waste management.
• PhD scholars, postdocs, and industry professionals with an interest in AI applications for environmental sustainability.
• Basic familiarity with Python and machine learning is recommended, but not required.