Welcome to the AI for Disaster Management (AI4DM) GitHub Organization

This is the official GitHub organization for the Advanced Study Institute on Artificial Intelligence for Disaster Management, a 7-day intensive course sponsored by the NATO Science for Peace and Security (SPS) Programme (Grant G8054).

The event will be held from November 17-25, 2025, at the University of Central Florida's Institute for Simulation and Training in Orlando, FL, USA.

About This Organization

This space serves as the central hub for all technical materials, code, datasets, and presentations related to the Advanced Study Institute. Our goal is to provide a lasting and accessible resource for all participants, both during and after the event.

Here you will find a collection of repositories dedicated to the various sessions and hands-on labs covered in the course.

Key Course Themes

The curriculum is structured into two main parts, with dedicated repositories for each:

• Part 1: Automated Building Damage Identification

  • Fundamentals of Image Classification & CNNs
  • Effective Training of Deep Learning Models
  • Hands-on Lab: Building a Damage Classification CNN with PyTorch

• Part 2: Automating Flood Event Detection Using Satellite Imagery

  • Machine Learning Workflows for Geospatial Data
  • Utilizing Hardware-Accelerated Tools (GPUs) for Efficient Processing
  • Hands-on Lab: Deploying a Deep Learning Model for Flood Detection

Navigating the Repositories

Each repository is named to correspond with a specific session or topic from the event programme. Inside each repository, you will find:

• Presentation slides (PDF)
• Jupyter Notebooks or Python scripts for hands-on labs
• Links to relevant datasets
• Additional learning resources and documentation

Event Information & Links

• Official Event Website: https://sites.google.com/view/nato-sps-ai4dm
• Contact the Organizers: For questions about the event, please email Dr. Bulent Soykan at Bulent.Soykan@ucf.edu.

This activity is sponsored by the NATO Science for Peace and Security Programme under grant G8054.