Course Code: gi3bs
Duration: 2 hours
Overview:

Objective: Equip the team with skills to develop and check code for geospatial intelligence applications, and apply generalized ML and LLMs to geospatial data.

Course Outline:

 

Agenda:

 

Introduction to Code Development and Checking (15 minutes)

  • Overview of best practices in code development and checking.
  • Importance of code quality in geospatial intelligence projects.

AI Tools for Code Checking (15 minutes)

  • Introduction to AI tools for code checking and development (e.g., GitHub Copilot, DeepCode, SonarQube).
  • Demonstration of a selected tool.

Hands-On Exercise: Developing and Checking Code (30 minutes)

  • Practical exercise: Use an AI tool to develop and check code for a geospatial application (e.g., a script for data preprocessing or analysis).
  • Review and refine the code based on AI feedback.

Applying Generalized ML and LLMs to Geospatial Data (30 minutes)

 
  • Introduction to generalized machine learning models and large language models (LLMs) in geospatial data.
  • Practical exercise: Use a pre-trained LLM (e.g., GPT-3) to analyze and extract insights from geospatial data.

Q&A and Future Directions (30 minutes)

  • Addressing questions and discussing the future potential of AI in geospatial intelligence.
  • Exploring additional AI tools and resources for further learning.

Materials Needed:

  • Laptops with internet access.
  • Access to AI tools for code development and checking (e.g., GitHub Copilot, DeepCode).
  • Access to LLMs (e.g., GPT-3 via OpenAI API).
  • Sample code and geospatial datasets for exercises.