AI in Remote Sensing for Environmental Protection
International Workshop on Smart Technologies for Environmental Monitoring and Disaster Risk Reduction
About This Course
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.
Aim
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.
Workshop Objectives
-
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
Workshop Structure
🔹 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)
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
Important Dates
Registration Ends
06/16/2025
IST 4 PM
Workshop Dates
06/16/2025 – 06/18/2025
IST 5:30 PM
Workshop Outcomes
-
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
Meet Your Mentor(s)
Fee Structure
Student Fee
₹1999 | $50
Ph.D. Scholar / Researcher Fee
₹2999 | $60
Academician / Faculty Fee
₹3999 | $70
Industry Professional Fee
₹5999 | $90
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →