GeoAI

Also published on LinkedIn. Those of us at the intersection of EO and AI still have a lot to bring, but further maturing a separate domain might not be the most impact-driven path. This is the article form of the talk I gave at #ECCV conference workship on Conservation and Climate change. Thank you Climate AI Nordics for the invitation. Hope I was able to sir the pot enough. Is geodata just data? We geo experts adopt most frontier techniques fast — SAM (“Segment Anything Model”) was applied to satellite imagery within three weeks, with fantastic applicability. Yet there is no evidence that the general-purpose frontier models (Gemini, GPT, Claude, DeepSeek, Kimi, GLM) treat Earth observation in any of the specific ways we in GeoAI do: feature engineering around metadata, rasters, projections, bands, and sensor physics. Models that do accept images expose only an RGB image interface, and while they can appear well versed and able to generate GIS code, the primitives that define GeoAI (e.g. geoattention) are absent from their published core architectures. I have reviewed eighteen frontier-lab model cards and technical reports from this year. Not one documents an Earth-observation input or an Earth-observation benchmark. This is absence of evidence, not evidence of absence, but it does show that geo is not a publicly stated architectural priority. ...

Google just launched “Earth AI.” Kudos and thank you to Christopher Phillips, James Manyika, and the team. The world is a better place today. They’ve also raised public awareness and set a higher bar for what planetary-scale awareness could and should be, and how AI can help get us there. Many have asked me for hot takes, since I’m also deep in geoAI. IMO Google also leaked that their geo moat is getting weaker. Their tech paper makes clear this is more about smart orchestration of (open) models than throwing compute at closed data. Earth data — “the other trillion tokens of AI” — is not incremental to text/images but orthogonal. ...

TL;DR: Training Clay v1.5 was “carbon neutral” and actually emitted ~10 tonnes of CO₂e. Moreover, focusing on lower emissions during geoAI training is a climate distraction compared to understanding geoembeddings. A year ago we trained Clay model v1.5 — still one of the most capable geoAI models today: open-source, open-data, open-license. At the time we promised to publish its emissions. I just updated the docs, but sharing this longer post since it proved harder — and had deeper pragmatic implications — than I expected. ...