AI in Remote Sensing for Environmental Protection

About This Course

“AI in Remote Sensing for Environmental Protection” is a transformative 3-week course designed to demonstrate how AI is revolutionizing environmental risk monitoring by integrating remote sensing, IoT sensor networks, satellite imagery, and predictive analytics. Participants will gain hands-on experience with cutting-edge tools and platforms like Google Earth Engine, YOLO/Deep Learning for image detection, scikit-learn for environmental datasets, and automated alert systems using Arduino/Raspberry Pi.

This course equips participants with the skills to detect, predict, and mitigate environmental hazards such as floods, wildfires, chemical leaks, and air pollution using advanced AI techniques.

Aim

This course aims to train participants in leveraging AI, Machine Learning (ML), and automation technologies to address environmental challenges. By providing a practical approach to real-time detection, prediction, and mitigation, participants will be prepared to contribute to global efforts in climate adaptation and disaster preparedness.

Course Objectives

• Introduce advanced AI and automation tools for environmental applications

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

• Promote interdisciplinary collaboration between technology and environmental sectors

• Enable participants to contribute to climate adaptation and disaster preparedness

• Provide open-access tools and datasets for continued innovation

Course Structure

🔹 Module 1: Fundamentals of Remote Sensing and AI Integration

Theme: Understanding the Basics of Remote Sensing and AI

• Introduction to Remote Sensing

  • Overview of satellite, UAV, and ground-based data acquisition

  • Key environmental applications: deforestation, biodiversity, water quality, air pollution

• 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

🔹 Module 2: Machine Learning Applications in Remote Sensing

Theme: Applying Machine Learning Techniques to Environmental Data

• 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

🔹 Module 3: Advanced Techniques and Real-World Applications

Theme: Integrating Deep Learning and AI in Conservation Efforts

• 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)

Who Should Enrol?

• AI for Earth/Environmental Researcher

• Disaster Risk Analyst using AI

• Environmental IoT System Developer

• Remote Sensing Data Scientist

• Smart City and Resilience Tech Advisor