About Workshop
Aim
What Participants Will Learn
- 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.
Structure
Day 1: Data-Driven Environmental Monitoring ā Foundations
Ā ā Introduction to Remote Environmental Sensing and Smart Monitoring
Ā ā Understanding Data Sources: Satellite Imagery, Air Quality Sensors, Water Data
Ā ā Exploring Moderate Reolution Imaging Spectroradiometer(MODIS) and Sentinel Satellite Datasets
Ā ā Hands-On: Visualizing Environmental Data in Google Colab (Pollution Mapping)
Ā ā Case Studies: Smart Monitoring Systems for Urban Sustainability
Day 2: Deep Learning for Climate and Pollution Pattern Detection
Ā ā Introduction to AI for Climate and Pollution Studies
Ā ā Image Segmentation Techniques for Land, Water, and Air Monitoring
Ā ā Training a CNN Model to Classify Land Cover or Detect Pollution Hotspots
Ā ā Hands-On: Building and Evaluating an Environmental Pattern Recognition Model
Ā ā Customizing Models for Specific Cities or Regions
Day 3: Smart Solutions ā AI-Driven Environmental Decision Support
Ā ā Introduction to AI-Powered Environmental Policy Tools
Ā ā Using Multi-source Data (Pollution, Traffic, Green Cover) for Decision Making
Ā ā Build Your Own: Smart City Environmental Health Dashboard (Streamlit App)
Ā ā Hands-On Model Deployment and Visualization (Google Colab/Streamlit)
Ā ā Use Cases: Urban Planning, Disaster Risk Reduction, Smart Greening Initiatives
Important Dates
Registration Ends
Workshop Dates
What You Will Gain
Outcomes
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.
Who Should Attend
- 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.