AI in Remote Sensing for Environmental Protection

This workshop explores how AI is transforming environmental risk monitoring by integrating remote sensing, IoT sensor networks, satellite imagery, and predictive analytics to enhance public safety and ecological resilience. Participants will gain hands-on experience with tools and platforms such as Google Earth Engine, YOLO/Deep Learning for image detection, scikit-learn for environmental datasets, and automated alert systems using Arduino/Raspberry Pi.

• 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

✅ 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 

🔹 Day 2: Machine Learning Applications in Remote Sensing

✅ 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 

🔹 Day 3: Advanced Techniques and Real-World Applications

✅ 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 

Discussion on deployment strategies (APIs, web apps)

• AI for Earth/Environmental Researcher

• Disaster Risk Analyst using AI

• Environmental IoT System Developer

• Remote Sensing Data Scientist

• Smart City and Resilience Tech Advisor