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.