Artwork

MapScaping에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 MapScaping 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Player FM -팟 캐스트 앱
Player FM 앱으로 오프라인으로 전환하세요!

Natural Language Geocoding

45:14
 
공유
 

Manage episode 431724295 series 2502116
MapScaping에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 MapScaping 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

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:

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.
  continue reading

239 에피소드

Artwork
icon공유
 
Manage episode 431724295 series 2502116
MapScaping에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 MapScaping 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

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:

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.
  continue reading

239 에피소드

すべてのエピソード

×
 
Loading …

플레이어 FM에 오신것을 환영합니다!

플레이어 FM은 웹에서 고품질 팟캐스트를 검색하여 지금 바로 즐길 수 있도록 합니다. 최고의 팟캐스트 앱이며 Android, iPhone 및 웹에서도 작동합니다. 장치 간 구독 동기화를 위해 가입하세요.

 

빠른 참조 가이드

탐색하는 동안 이 프로그램을 들어보세요.
재생