In this episode, I welcome Jason Gilman, a Principal Software Engineer at Element 84, to explore the exciting world of natural language geocoding.

Key Topics Discussed:

Introduction to Natural Language Geocoding:

• Jason explains the concept of natural language geocoding and its significance in converting textual descriptions of locations into precise geographical data. This involves using large language models to interpret a user's natural language input, such as "the coast of Florida south of Miami," and transform it into an accurate polygon that represents that specific area on a map. This process automates and simplifies how users interact with geospatial data, making it more accessible and user-friendly.

The Evolution of AI and ML in Geospatial Work:

• Over the last six months, Jason has shifted focus to AI and machine learning, leveraging large language models to enhance geospatial data processing.

Challenges and Solutions:

• Jason discusses the challenges of interpreting natural language descriptions and the solutions they've implemented, such as using JSON schemas and OpenStreetMap data.

Applications and Use Cases:

• From finding specific datasets to processing geographical queries, the applications of natural language geocoding are vast. Jason shares some real-world examples and potential future uses.

Future of Geospatial AIML:

• Jason touches on the broader implications of geospatial AI and ML, including the potential for natural language geoprocessing and its impact on scientific research and everyday applications.

Interesting Insights:

• The use of large language models can simplify complex geospatial queries, making advanced geospatial analysis accessible to non-experts.

Quotes:

• "Natural language geocoding is about turning a user's textual description of a place on Earth into a precise polygon."

Additional Resources:

• Element 84 Website

Connect with Jason:

• Visit Element 84's website for more information and contact details.