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:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.
• Integration of AI and machine learning with traditional geospatial tools opens new avenues for research and application, from environmental monitoring to urban planning.

Quotes:

• "Natural language geocoding is about turning a user's textual description of a place on Earth into a precise polygon."
• "The combination of vision models and large language models allows us to automate complex tasks that previously required manual effort."

Additional Resources:

• Element 84 Website
• State of the Map US Conference Talk on YouTube
• Blog Posts on Natural Language Geocoding

Connect with Jason:

• Visit Element 84's website for more information and contact details.
• Google "Element 84 Natural Language Geocoding" for additional resources and talks.