AI and Automation in Environmental Hazard Detection

• Introduce advanced AI and automation tools for environmental applications

• Bridge gaps between climate data, sensors, and AI models

• Promote interdisciplinary collaboration between tech and environmental fields

• Enable participants to contribute to climate adaptation and disaster preparedness

• Provide open-access tools and datasets for continued innovation

📅 Day 1: Introduction to AI in Environmental Hazard Detection

🔹 Environmental Hazards & AI's Role

• Overview of hazards (pollution, natural disasters)
• AI’s transformative capabilities in detection & prediction

🔹 AI & Machine Learning Fundamentals

• Key ML techniques for hazard analysis
• Data preprocessing & algorithm selection

🔹 🛠️ Hands-On Lab: AI Tools for Hazard Detection

• Setting up AI environments
• Building basic predictive models

📅 Day 2: Advanced AI Techniques for Hazard Detection

🔹 Remote Sensing & AI for Monitoring

• Leveraging satellite/drone data + GIS integration
• AI-powered hazard tracking

🔹 Deep Learning for Hazard Detection

• Optimizing CNNs & RNNs for environmental threats
• Model training & performance tuning

🔹 🛠️ Hands-On Lab: Remote Sensing Data for Detection

• Processing real-world satellite imagery
• Training deep learning models

📅 Day 3: Automation & Real-Time Monitoring

🔹 AI & IoT for Real-Time Monitoring

• Sensor networks + AI for instant hazard alerts
• Edge computing for rapid response

🔹 Automating Hazard Detection & Response

• AI-driven decision pipelines
• Case studies in wildfire/flood prediction

🔹 🛠️ Hands-On Lab: Building a Real-Time System

• Developing an automated detection prototype
• Simulating emergency alert scenarios

• Understand the role of AI in real-time environmental monitoring

• Apply ML models to real-world environmental datasets

• Build simple automated alert systems using IoT and AI

• Analyze satellite and sensor data for environmental insights

• Contribute to sustainable development and early warning innovations

• Environmental scientists and disaster management professionals

• Data scientists and engineers working in sustainability

• Urban planners, civil engineers, and remote sensing experts

• Government agency representatives and NGO workers

• UG/PG/PhD students in environmental science, AI, or geoinformatics