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/
Being Visible In The Geospatial Community
GDAL - Geospatial Data Abstraction Library
Counting Animals Using Satellite imagery
Access to data - making room for unexpected contributors
Geospatial Innovation - what it might look like
Super Resolution - smarter upsampling
OpenStreetMap is a community of communities
Open Geospatial Standards - shared standards to solve shared problems
Introducing Google Earth Engine
Getting Where You Want To Go In Your Geospatial Career
The Earth Archive
Introduction to Synthetic-aperture Radar (SAR)
From GIS Analyst to Software Engineer
Building a 4D "Digital Twin" of the Planet
Skills, Leadership, Mentorship and the Geospatial Community
Navigating The Past, Present And Future Of GNSS
Mapping The Ocean Floor
Being A Professional Geographer
How To Augment Reality
Satellite-based Augmentation System - A base station in the sky
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Regenerative Agriculture Podcast
Bedrock: Earth’s Earliest History
The Great Simplification with Nate Hagens
Kosmographia
Climate One