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eli knaap
@knaaptime.com

among the many distinctions in *geospatial analysis*, it seems like physical geographers use "AI" in the 'traditional' sense of, like, deep nets applied to imagery data. whereas human geographers use it in the LLM sense of, like 'please vibecode my work for me because i asked so nicely'

Qiusheng Wu@giswqs.bsky.social

Open Geospatial Workshop happening at #AGU25 in Room 333 today! I will be teaching interactive mapping and GeoAI from 9:45-11:45 AM. Registration is required is to attend, but I heard that they will be checking badges at the entrance until 9:30 AM.

eli knaap
@knaaptime.com

in other words, this: "geo" is already such a huge umbrella that 'geoai', as a term, is basically meaningless

Timnit Gebru@timnitgebru.blacksky.app

Wildly different things, tasks, techniques, subspecialties being lumped into "AI" and then being conflated with each other, doesn't help. Different types of models vs the techniques to train them vs the tasks they are supposed to accomplish, all under "AI".

eli knaap
@knaaptime.com

*chefs kiss* remote sensing is AI now, because $

Timnit Gebru@timnitgebru.blacksky.app

Is logistic regression "AI"? What about convex optimization? Is LLM AI? What about an image recognition system built without using "feature learning" techniques but done through "feature engineering"?

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