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/
Openlayers - Geospatial JavaScript
My Story, my why
GRASS GIS probably doesn’t get the attention it deserves
Machine learning and object detection for the rest of us
Raster Frames - making imagery a first class citizen
GIS education and training, online and in-person
Cloud Detection- an open problem
A Business Built On Open Source GIS
location Privacy and Data Ethics
Communicating with maps - The art of cartography
Being self employed in the earth observation sector
Spatial SQL - GIS without the GIS
Elastic Search
Self-employment in the GIS / Geospatial industry
The long tail of geospatial and spatial thinking
COVID-19 Spatial Research
Open Source GPU Processing
H3 geospatial indexing system
Google BigQuery GIS - Geospatial in the Cloud
A one to one map of the world
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