AI for Environmental Monitoring and Sustainability

• Build AI literacy in environmental monitoring applications.
• Provide hands-on experience with tools like Google Colab and Streamlit.
• Enable participants to use satellite and sensor data for sustainable insights.
• Foster the development of smart, AI-driven environmental solutions.

Day 1: Data-Driven Environmental Monitoring – Foundations

 ● Introduction to Remote Environmental Sensing and Smart Monitoring
 ● Understanding Data Sources: Satellite Imagery, Air Quality Sensors, Water Data
 ● Exploring Moderate Reolution Imaging Spectroradiometer(MODIS) and Sentinel Satellite Datasets
 ● Hands-On: Visualizing Environmental Data in Google Colab (Pollution Mapping)
 ● Case Studies: Smart Monitoring Systems for Urban Sustainability

Day 2: Deep Learning for Climate and Pollution Pattern Detection

 ● Introduction to AI for Climate and Pollution Studies
 ● Image Segmentation Techniques for Land, Water, and Air Monitoring
 ● Training a CNN Model to Classify Land Cover or Detect Pollution Hotspots
 ● Hands-On: Building and Evaluating an Environmental Pattern Recognition Model
 ● Customizing Models for Specific Cities or Regions

Day 3: Smart Solutions – AI-Driven Environmental Decision Support

 ● Introduction to AI-Powered Environmental Policy Tools
 ● Using Multi-source Data (Pollution, Traffic, Green Cover) for Decision Making
 ● Build Your Own: Smart City Environmental Health Dashboard (Streamlit App)
 ● Hands-On Model Deployment and Visualization (Google Colab/Streamlit)
 ● Use Cases: Urban Planning, Disaster Risk Reduction, Smart Greening Initiatives

By the end of the workshop, participants will:

• Understand AI tools for environmental data acquisition and processing.
• Develop and deploy AI models for climate and pollution analysis.
• Create and visualize real-time dashboards for smart decision-making.
• Be equipped to contribute to data-driven environmental innovation.

• Graduate and postgraduate students of environmental science, data science, Artificial Intelligence/Machine Learning, and sustainability domains.
• Professionals, researchers, and environmental consultants.
• Urban planners, climate activists, and decision-makers interested in technology-driven sustainability.