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FOSS4G NA 2024 - Embed All The Things: The Promise Of Geospatial Vector Embeddings - Adeel Hassan
Manage episode 461965962 series 3234430
Content provided by Project Geospatial. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Project Geospatial or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Adeel Hassan discusses the significance of geospatial vector embeddings derived from imagery, highlighting their potential in the geospatial domain through open-source models and tools. Highlights π Vector embeddings are crucial for analyzing high-dimensional geospatial data. π§ They represent data points in a lower-dimensional space, revealing similarities and dissimilarities. π Applications include clustering similar images and detecting changes over time. π Text-image embeddings enable natural language search based on image content. π Open-source models like Sky Clip enhance functionality for geospatial applications. π Seasonal variations in embeddings can indicate environmental changes and events like floods. π οΈ The technology is still evolving, presenting both opportunities and challenges. For more content like this check out www.projectgeospatial.com #Geospatial #MachineLearning #VectorEmbeddings #OpenSource #DataAnalysis #RemoteSensing #AI
362 episodes
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Manage episode 461965962 series 3234430
Content provided by Project Geospatial. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Project Geospatial or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Adeel Hassan discusses the significance of geospatial vector embeddings derived from imagery, highlighting their potential in the geospatial domain through open-source models and tools. Highlights π Vector embeddings are crucial for analyzing high-dimensional geospatial data. π§ They represent data points in a lower-dimensional space, revealing similarities and dissimilarities. π Applications include clustering similar images and detecting changes over time. π Text-image embeddings enable natural language search based on image content. π Open-source models like Sky Clip enhance functionality for geospatial applications. π Seasonal variations in embeddings can indicate environmental changes and events like floods. π οΈ The technology is still evolving, presenting both opportunities and challenges. For more content like this check out www.projectgeospatial.com #Geospatial #MachineLearning #VectorEmbeddings #OpenSource #DataAnalysis #RemoteSensing #AI
362 episodes
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