Foundation Models

My key message on Monday at the geo day at AWS in London was that a fundamental shift in how we approach Earth Observation (EO) data is not just possible, but essential, with Geo Embeddings at its core. Phil Cooper, the host, introduced the session around the question of whether GIS has an identity crisis. I think that is not only true, but also that is because GIS has had an EO product problem for decades and finally AI seems to provide the stack to solve it… We must change GIS EO from pixel math and widgets, to frictionless semantic navigation through embeddings. ...

AI is changing the world of text, images, audio, … but not Earth data. Yes, its hard to work with, but not only we are dropping the [globe] ball, AI for Earth has outsized benefits (impact and profits alike), specially if done fully in the open. Last year Dan Hammer and I noticed a glaring oversight on the current tsunami of AI: the largely untapped potential for Earth. We have amazing breakthroughs in AI with text, images, video, and audio — but not Earth data. This is deeply disappointing considering the massive global challenges we face related to nature, climate change, and sustainability. I think part of the reason for this gap in AI is that AI and geospatial skills are the bottleneck. Earth data is very difficult to store, process, and work with. So AI+Geospatial is an extremely niche set of skills. ...