graph LR
A[Day 1<br/>EO Data &<br/>Fundamentals] --> B[Day 2<br/>Machine<br/>Learning]
B --> C[Day 3<br/>Deep<br/>Learning]
C --> D[Day 4<br/>Advanced<br/>Topics]
style A fill:#003399,stroke:#003399,stroke-width:3px,color:#fff
style B fill:#f8f9fa,stroke:#dee2e6,stroke-width:2px,color:#495057
style C fill:#f8f9fa,stroke:#dee2e6,stroke-width:2px,color:#495057
style D fill:#f8f9fa,stroke:#dee2e6,stroke-width:2px,color:#495057
CoPhil EO AI/ML Training
4-Day Advanced Training on AI/ML for Earth Observation
CoPhil EO AI/ML Training Programme
4-Day Advanced Training on AI/ML for Earth Observation
Advanced training for Philippine EO professionals on AI/ML applications for Disaster Risk Reduction, Climate Change Adaptation, and Natural Resource Management
About This Training
Welcome to the 4-Day Advanced Online Training on AI/ML for Earth Observation for Philippine EO Professionals. This comprehensive training is part of the CoPhil Programme (EU-Philippines Copernicus Capacity Support Programme), a flagship initiative under the European Union’s Global Gateway strategy.
NoteTraining Details
Dates: October 20-23, 2025 Instructor: Stylianos Kotsopoulos Programme: EU-Philippines CoPhil Programme
This course strengthens the Philippines’ capacity to use Copernicus Earth Observation data for:
- Disaster Risk Reduction (DRR) - Flood mapping, typhoon monitoring, landslide assessment
- Climate Change Adaptation (CCA) - Drought monitoring, agricultural resilience, coastal changes
- Natural Resource Management (NRM) - Forest monitoring, land cover mapping, marine resources
Course Curriculum
This intensive 4-day program takes you from EO data fundamentals to deploying operational AI/ML solutions. Each day builds on previous concepts through hands-on exercises using Philippine case studies.
Format: 4 days × 4 sessions × 2 hours = 32 hours total | Mode: Online via Google Colab | Level: Intermediate to Advanced
Learning Journey
What You’ll Achieve
By completing this training, you will:
🎯 Master EO Data - Work confidently with Sentinel-1 SAR and Sentinel-2 optical data, understanding their characteristics and Philippine EO infrastructure (SIYASAT, NAMRIA, DOST-ASTI). Navigate the Copernicus Data Space Ecosystem and implement preprocessing pipelines.
🤖 Build AI/ML Models - Design, train, and evaluate machine learning (Random Forest, SVM) and deep learning models (CNNs, U-Net, YOLO) for land cover classification, flood mapping, and disaster monitoring. Master model validation, hyperparameter tuning, and deployment workflows.
🚀 Deploy Solutions - Create scalable processing pipelines using cloud platforms and integrate with Philippine operational systems. Implement real-time monitoring solutions for DRR, CCA, and NRM applications.
🌏 Apply to Real Challenges - Complete hands-on projects addressing Philippine environmental priorities using real datasets from Palawan (land cover), Central Luzon (flood mapping), Mindanao (drought monitoring), and Metro Manila (urban monitoring).
Daily Breakdown
01
EO Data & AI/ML Fundamentals
4 sessions × 2 hours
What you’ll learn: Copernicus Sentinel missions • Philippine EO ecosystem (PhilSA, DOST-ASTI, NAMRIA) • AI/ML core concepts • Python geospatial libraries (GeoPandas, Rasterio) • Google Earth Engine
Hands-on practice: Python geospatial data processing, GEE data access, visualization workflows
02
Machine Learning for EO
4 sessions × 2 hours
What you’ll learn: Random Forest & SVM classification • K-means clustering • Feature engineering for spectral indices • CNN fundamentals • Transfer learning concepts
Hands-on practice: Palawan land cover classification, model evaluation, feature importance analysis
03
Deep Learning for EO
4 sessions × 2 hours
What you’ll learn: U-Net semantic segmentation • Object detection (YOLO, Faster R-CNN) • SAR flood mapping (Central Luzon) • Building detection & damage assessment • Transfer learning strategies
Hands-on practice: U-Net flood mapping implementation, YOLO building detection, model fine-tuning
04
Advanced Topics & Projects
4 sessions × 2 hours
What you’ll learn: LSTMs for time series • Drought monitoring (Mindanao) • Foundation models (NASA-IBM Prithvi, Clay) • Self-supervised learning • Explainable AI (XAI)
Hands-on practice: Time series forecasting, foundation model fine-tuning, capstone project development
Prerequisites
Get ready for the training with these simple requirements:
Required
Technical Setup:
- Google account (for Colab and Earth Engine)
- Google Earth Engine account (free signup)
- Basic Python knowledge (variables, loops, functions)
- Modern web browser (Chrome or Firefox)
TipNo Installation Needed!
All exercises run in Google Colaboratory. No local software installation required.
Recommended Background
Helpful Knowledge:
- Basic remote sensing concepts (bands, resolution, sensors)
- Familiarity with machine learning terminology
- Understanding of Philippine geography and environmental challenges
- Experience with Jupyter notebooks (helpful but not required)
NoteNew to EO or ML?
Don’t worry! Day 1 covers all foundational concepts. We start from the basics.
Ready to get started? Follow our step-by-step setup guide to prepare your environment.
Course Resources
Everything you need for a successful learning experience:
Setup Guide
Complete technical setup instructions for Google Colab, Earth Engine, and required accounts.
Philippine EO Resources
Directory of Philippine EO platforms, agencies, and data access portals.
Download Materials
Access all course notebooks, datasets, presentations, and supplementary materials.
FAQ
Common questions and troubleshooting tips for technical issues and course content.
Glossary
Comprehensive definitions of EO, AI/ML, and geospatial terminology used throughout the course.
Cheat Sheets
Quick reference guides for Python, geospatial libraries, and machine learning workflows.
Questions & Support
Ask questions, report issues, or discuss course content with instructors and fellow participants.
About CoPhil Programme
TipTechnical Assistance for Philippines’ Copernicus Capacity Support
The CoPhil Programme is part of the EU-Philippines cooperation programme and the EU’s Global Gateway strategy.
Key Partners:
- Philippine Space Agency (PhilSA) - Co-chair and space data authority
- Department of Science and Technology (DOST) - Co-chair and technology advancement
- European Union - Funding and technical cooperation
- European Space Agency (ESA) - Copernicus programme expertise
Programme Objectives:
- Establish Copernicus Mirror Site in the Philippines
- Build capacity in EO data analysis and AI/ML applications
- Co-develop pilot services for DRR, CCA, and NRM
- Create sustainable Digital Space Campus for continued learning
- Foster Philippine EO community of practice
Course Delivery
NoteTraining Format
- Language: English
- Certificate: Issued upon completion of all days and capstone project
- Support: Live instructors and teaching assistants
Getting Started
ImportantReady to Begin?
- Complete Setup - Follow our Setup Guide
- Review Prerequisites - Ensure you have the required accounts
- Start Day 1 - Begin with foundational concepts
- Ask Questions - Use FAQ and instructor support throughout
Need Help?
TipAsk Questions & Get Support
Have a question about the course content or facing technical issues?
Visit our GitHub Issues page to:
- Ask questions about course material, exercises, or concepts
- Report technical issues with notebooks, data access, or setup
- Share insights and discuss solutions with fellow participants
- Get help from instructors and teaching assistants
- Search existing discussions - your question may already be answered!
Throughout the training, you can also:
- Ask questions during live sessions
- Consult the FAQ for common issues
- Check the Glossary for term definitions
- Download Cheat Sheets for quick reference
- Access the Philippine EO Resources directory
Technical Support
NoteGetting Help
For technical issues: - Questions & Issues: Post on GitHub Issues for community support - Google Colab problems: See Setup Guide - Data access issues: Check session-specific troubleshooting - Course questions: Ask during live sessions or post on GitHub Issues - Email support: skotsopoulos@neuralio.ai
Acknowledgments
This training is made possible through the partnership between:
- European Union (Global Gateway Initiative)
- Philippine Space Agency (PhilSA)
- Department of Science and Technology (DOST)
- European Space Agency (ESA)
- CoPhil Programme Consortium
Funded by the European Union under the Global Gateway initiative and delivered in partnership with the Philippine Space Agency (PhilSA) and the Department of Science and Technology (DOST).