Machine Learning on Geospatial Data with Malte Loller-Anderson & Mathilde Ørstavik
.NET Rocks!29 Aug 2024

Machine Learning on Geospatial Data with Malte Loller-Anderson & Mathilde Ørstavik

What can machine learning do for geospatial data? Carl and Richard talk to Malte Loller-Anderson and Mathilde Ørstavik about their work at Norkart, using aerial imagery to build detailed maps around Norway. Mathilde dives into the critical role of machine learning - identifying buildings in images. Usually done by hand with each new image, Norkart has a machine learning model that automates the process trained on previous vector maps of buildings. But there are many things that look like buildings in Norway, including patches of snow, mountains, and even shapes under water. Malte also discusses how Norkart has decided to train in-house with nVidia L40 processors rather than in the cloud - the hardware is used 24 hours a day since some models can take weeks to train! There are many interesting ideas about geospatial data and machine learning from people who have been doing it for years.

Avsnitt(1957)

Tim Huckaby Live in Tulsa

Tim Huckaby Live in Tulsa

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29 Apr 201043min

Michelle Leroux Bustamante Live in Dallas

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Alan Griver, Beth Massi, and Tim Heuer Live in Phoenix

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Scott Stanfield Live in Redlands, CA

Scott Stanfield Live in Redlands, CA

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23 Apr 201052min

Billy Hollis Live in San Diego

Billy Hollis Live in San Diego

Carl and Richard talk to Billy Hollis in San Diego on the third stop of the road trip. Billy always has something interesting to talk about.Support this podcast at — https://redcircle.com/net-rocks/donations

22 Apr 201048min

Dan Fernandez Live in Los Angeles

Dan Fernandez Live in Los Angeles

Carl and Richard talk to Dan Fernandez about Channel 9, online community, Visual Studio Express, and more on the second stop of our Road Trip!Support this podcast at — https://redcircle.com/net-rocks/donations

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Phil Haack Live in Mountain View, California

Phil Haack Live in Mountain View, California

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20 Apr 201043min

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