AI in Remote Sensing for Environmental Protection

To train participants in leveraging Artificial Intelligence (AI), Machine Learning (ML), and automation technologies for real-time detection, prediction, and mitigation of environmental hazards, from floods and wildfires to chemical leaks and air pollution.

• 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: Fundamentals of Remote Sensing and AI Integration

• Overview of satellite, UAV, and ground-based data acquisition 
• Key environmental applications: deforestation, biodiversity, water quality, air pollution 

• Basics of supervised and unsupervised learning 
• Data formats: raster, vector, hyperspectral, multispectral 

• Accessing and visualizing remote sensing datasets using Python (rasterio, geopandas) 
• Preprocessing and cleaning satellite imagery (cloud masking, radiometric corrections) 
• Setting up Jupyter Notebook for analysis 

🔹 Day 2: Machine Learning Applications in Remote Sensing

• Classification algorithms: Decision Trees, Random Forests, SVM 
• Image segmentation and object detection basics 

• Extracting features: NDVI, land cover classes, change detection 
• Model evaluation metrics: accuracy, confusion matrix, IoU 

• Training classification models to detect land use and cover change 
• Implementing Random Forest for deforestation detection using scikit-learn 
• Visualizing results on interactive maps with Folium or Plotly 

🔹 Day 3: Advanced Techniques and Real-World Applications

• CNNs for object detection in satellite imagery 
• Transfer learning with pre-trained models (ResNet, UNet) 
• Challenges in multi-source, multi-temporal data integration 

• Deploying AI models for conservation monitoring 
• Ethical considerations: bias, data governance, privacy 
• Linking AI insights to policy and environmental management 

• Applying a CNN to classify deforestation in satellite images 
• Visualizing predictions and creating an interactive dashboard with Streamlit 

Discussion on deployment strategies (APIs, web apps)

• 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

• AI for Earth/Environmental Researcher

• Disaster Risk Analyst using AI

• Environmental IoT System Developer

• Remote Sensing Data Scientist

• Smart City and Resilience Tech Advisor