This presentation explores the innovative integration of Large Language Models (LLMs) into geospatial applications, focusing on vector data and location intelligence. Unlike traditional computer vision or imagery-based machine learning, this session delves into how LLMs can enhance the querying, analysis, and interpretation of geospatial tabular data.

1. Metadata Enrichment: Learn how LLMs can enhance metadata, improving the discoverability and usability of your geospatial datasets.
2. Natural Language Queries: Discover how LLMs can interpret and respond to natural language queries, making geospatial data more accessible to non-technical users.
3. Semantic Search: Understand how LLMs can elevate full-text search by comprehending the context and meaning of your queries, enabling more accurate and relevant results.
4. Intent Recognition: Explore how LLMs can discern user intent, whether it's displaying data on a map, searching for a dataset, or performing an analysis.
5. Reasoning and Function Calling: See how LLMs can leverage reasoning and function calling to address complex geospatial queries and tasks.
6. Conversational Interaction: Experience how LLMs can engage in conversational interactions, providing a more intuitive user experience.
7. Text-to-SQL Transformation: Witness how LLMs can convert natural language requests into precise data queries, including geospatial filtering and aggregation.

The demonstrations and examples in this presentation are built upon Free and Open Source geospatial solutions and adhere to OGC standards, ensuring interoperability and accessibility.

By the end of this presentation, attendees will gain a fresh perspective on the potential of LLMs in geospatial applications, with practical insights and real-world applications that can transform how you interact with and leverage your geospatial data.