# Natural Language Geocoding

Page: https://stenobird.com/podcast/the-mapscaping-podcast-gis-geospatial-remote-sensing-earth-observation-and-digital-geography-743634/natural-language-geocoding
Text version: https://stenobird.com/podcast/the-mapscaping-podcast-gis-geospatial-remote-sensing-earth-observation-and-digital-geography-743634/natural-language-geocoding.md
Podcast: [The MapScaping Podcast - GIS, Geospatial, Remote Sensing, earth observation and digital geography](https://stenobird.com/podcast/the-mapscaping-podcast-gis-geospatial-remote-sensing-earth-observation-and-digital-geography-743634)
Published: 2024-08-01T01:00:57+00:00
Episode link: https://mapscaping.podbean.com/e/natural-language-geocoding/
Audio file: https://mcdn.podbean.com/mf/web/t44b4deawjmti5a9/Natural_Language_Geocoding6yiv3.mp3
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JSON: https://stenobird.com/v1/public/podcasts/the-mapscaping-podcast-gis-geospatial-remote-sensing-earth-observation-and-digital-geography-743634/episodes/natural-language-geocoding
Duration seconds: 2714

## Resource

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. Integration of AI and machine learning with traditional geospatial tools opens new avenues for res…

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