AI in Remote Sensing for Environmental Protection

✅ Module 1: Introduction to Remote Sensing

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

✅ Module 2: Introduction to AI in Remote Sensing

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

✅ Hands-On Lab:

• 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 

✅ AI Models for Environmental Data

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

✅ Feature Engineering and Model Evaluation

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

✅ Hands-On Lab:

• 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 

✅ Deep Learning in Remote Sensing

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

✅ From Models to Conservation Action

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

✅ Hands-On Lab:

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

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