RAG + LLM for Geospatial Applications

RAG in LLM stands for Retrieval-Augmented Generation. It's a technique that supercharges Large Language Models (LLMs) by incorporating external knowledge sources. RAG is generally used for making an LLM an expert in a specific domain.

LLMs traditionally work with text data, while geospatial applications deal with a different format - images, points, lines, polygons, and 3D models.

Despite this difference, LLMs + RAG can be surprisingly useful for the geospatial industry in a few ways:

1) Unlocking Insights from Textual Data (location information in property records, environmental reports, social media data about places, and even news articles etc)

2) Natural Language Interaction with the Geospatial Application.

3) Enhancing Reports and Communication.

4) RAG combines retrieval systems that find relevant geospatial data with LLMs.

RAG + LLM in Geospatial is a developing area of research and the combination of both have the potential to significantly impact the geospatial industry.