There is a general understanding that it is becoming increasingly difficult to extract meaning from all the data we are collecting without using AI.
But what is AI, and how did we end up in a situation where it is identifying wolves from dogs based on the presence of snow in the background of images?
What does this mean for spatial analysis using tabular data?
What is explainability?
This is not a "how-to" do spatial analysis using an AI episode, it is an overview of AI in spatial analysis episode with Vin Sharma, VP of Engineering at FourSquare
https://www.linkedin.com/in/ciphr/
https://foursquare.com/
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Distributing Geospatial Data
Geospatial Support for the UN World Food Programme
Aerial Imagery: The State Of The Art
The technology stack and the cultural stack
ChronoCards - Building a Business on ArcGIS Pro
Geospatial Consulting - As A Business And A Career
How Google Calculates Your Location
Reduce and Reverse Tropical Forest Loss With NICFI
Cloud Optimized Point Clouds
Full Stack Cartography
[From the Archive] An Introduction To Artificial Intelligence
Mid-Career Change
Peer To Peer Mapping and Digital Democracy
Thermal Imagery From Space
I Quit My Job
Monitoring Atmospheric Pollution From Space
Hex Tiles
The Business of Web Maps
Finding Water Leaks From Space
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Bedrock: Earth’s Earliest History
The Great Simplification with Nate Hagens
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Climate One