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#26 SpaceNet 6 Challenge Results: Multi-Sensor All-Weather Mapping

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Manage episode 267307295 series 2492216
Content provided by CosmiQ Works. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CosmiQ Works 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.

SpaceNet is a non-profit dedicated to accelerating open source, applied research in geospatial machine learning. In this episode, CosmiQ’s Ryan Lewis, Jake Shermeyer, and Daniel Hogan discuss the SpaceNet 6 Challenge where participants were asked to automatically extract building footprints with computer vision and AI algorithms using a combination of synthetic aperture radar (SAR) and electro-optical imagery. Hear about the challenge’s winning artificial intelligence models and the tradeoff between inference speed and model performance.

SpaceNet is made possible by co-founder and managing partner, CosmiQ Works; co-founder and co-chair, Maxar Technologies; and all the other Partners: Amazon Web Services (AWS), Capella Space, Topcoder, IEEE Geoscience and Remote Sensing (GRSS), the National Geospatial-Intelligence Agency, and Planet.

Learn more at www.spacenet.ai, and at the DownLinQ (https://medium.com/the-downlinq)

  continue reading

30 episodes

Artwork
iconShare
 
Manage episode 267307295 series 2492216
Content provided by CosmiQ Works. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CosmiQ Works 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.

SpaceNet is a non-profit dedicated to accelerating open source, applied research in geospatial machine learning. In this episode, CosmiQ’s Ryan Lewis, Jake Shermeyer, and Daniel Hogan discuss the SpaceNet 6 Challenge where participants were asked to automatically extract building footprints with computer vision and AI algorithms using a combination of synthetic aperture radar (SAR) and electro-optical imagery. Hear about the challenge’s winning artificial intelligence models and the tradeoff between inference speed and model performance.

SpaceNet is made possible by co-founder and managing partner, CosmiQ Works; co-founder and co-chair, Maxar Technologies; and all the other Partners: Amazon Web Services (AWS), Capella Space, Topcoder, IEEE Geoscience and Remote Sensing (GRSS), the National Geospatial-Intelligence Agency, and Planet.

Learn more at www.spacenet.ai, and at the DownLinQ (https://medium.com/the-downlinq)

  continue reading

30 episodes

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