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/
The Spatial Internet of Things
Spatially Enabling Customer Relations
Everything happens some where & some time - Spatiotemporal data and GIS
Polygons of Ownership
Jupyter notebooks for geospatial
Radio Frequency Data Collection
Hyper-accurate indoor location
Digital Twins
Geospatial Python
Two mobile data collection apps you need to know about
Rebranding GIS Geospatial
GIS vs Computer-aided design - everything you have always wanted to know #geospatial
Robotics, remote sensing and facilities management - #GIS #geospatial
Advice for job seekers from a geospatial recruitment expert
The overlap between Business Intelligence tools and GIS software
Dynamic vector tiles straight from the database
Earth Observation, platforms and data #geography #geospatial #remotesensing
Disrupting ESRI - Why it is hard to build a big geospatial business
QGIS - An opensource geospatial project
A Case Study in GIS
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Regenerative Agriculture Podcast
Bedrock: Earth’s Earliest History
The Great Simplification with Nate Hagens
Kosmographia
Climate One