09-11, 11:00–11:30 (America/Chicago), Grand G
Explore the use of SotA FOSS LLMs to automate metadata generation for geospatial imagery. Learn integration techniques for rapid metadata extraction, enhancing analysis with the power of open-source solutions.
In this talk we dive into the image inferencing capabilities of FOSS Large Language Models (LLMs), specifically focusing on geospatial satellite imagery. Our objective is to showcase how these advanced models can be smoothly integrated into data pipelines to automate the generation of image metadata.
Key Topics Covered:
Technical Guide:
1. Image Processing Workflow: A detailed explanation of the workflow used to generate metadata from satellite images, such as the use of bounding boxes and iterative prompts to extract features.
2. Step-by-Step Integration: A practical guide on how to incorporate FOSS LLMs into existing geospatial data pipelines.
3. Customization and Flexibility: A demonstration of how tuning the LLM’s prompts can tailor the metadata extraction to meet specific needs, providing flexibility beyond traditional deep learning techniques.
Advantages of FOSS LLMs:
1. Efficiency and Speed: Highlighting how these models can perform complex inferencing tasks quickly.
2. Flexibility in Metadata Extraction: Emphasizing the ease of adapting the model to extract different types of metadata through simple prompt adjustments.
3. Open-Source Benefits: Discussing the advantages of using open-source solutions, including transparency, community support, and the ability to customize and extend the models.
Challenges and Solutions:
1. Technical Challenges: Discussion of technical challenges encountered and how they were addressed.
2. Best Practices: Sharing lessons learned and best practices for implementing FOSS LLMs in geospatial workflows.
Future Directions:
1. Automation for Real-Time Analysis: Exploring potential extensions such as automating the capture of bounding boxes in real-time drone footage.
2. Advanced Parsing Techniques: Using FOSS tools to parse LLM outputs to extract specific details into structured data formats, enhancing the beginning and end stages of the data pipeline.
Impact and Significance:
1. Enhancing Geospatial Analysis: How automating metadata generation with FOSS LLMs can significantly enhance geospatial data analysis.
2. Broader Applications: Potential applications and benefits of this technology in various fields beyond geospatial analysis.
Attendees will leave with a thorough understanding of how to integrate FOSS LLMs into their workflows to automate and enhance image metadata generation, as well as a curiosity and interest in further exploring the capabilities of these advanced models. By the end of the session, participants will be equipped with the knowledge and tools to implement FOSS LLMs in their own projects, thereby enhancing their analytic capabilities and efficiency in handling geospatial data.
Join us to explore the forefront of automated metadata generation and discover how FOSS LLMs can revolutionize your geospatial workflows with the power of open-source innovation.
