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It is just open source version [1] of that, I assume. It's a visualization, but data is their own. earth.nullschool is visualization of NOAA's GFS model.
[1] https://github.com/cambecc/earth
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We build the equivalent for land, as a non-profit. It's basically a geo Transformer MAE model (plus DINO, plus matrioska, plus ...), but largest and most trained (35 trillion pixels roughly). Most importantly fully open source and open license. I'd love to help you replace land masks with land embeddings, they should significantly help downscale the local effects (e.g. forest versus city) that afaik most weather forecast simplify with static land cover classes at most. https://github.com/Clay-foundation/model
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What image size embeddings from what instrument?
We did try to relate OSM tags to Clay embeddings, but it didn't scale well. We did not give up, but we are re-considering ( https://github.com/Clay-foundation/earth-text ). I think SatClip plus OSM is a better approach. or LLM embeddings mapped to Clay embeddings...
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SkyScript
Official repo for "SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing"
Hey hey! We tried Clay v1 with 768 embeddings size using your tutorials. We then split NAIP SF to chips and indexed them. Afterwards, we performed image-to-image similarity search like in your explorer.
We tried to search for bridges, beaches, tennis courts, etc. It worked, but it didn't work well. The top of the ranking was filled with unrelated objects. We found that similarity scores are stacked together too much (similarity values are between 0.91 and 0.92 with 4 digit difference, ~200k tiles), so the encoder made very little difference between objects.
I believe that Clay can be used with additional fine-tuning for classification and segmentation, but standalone embeddings are pretty poor.
Check this: https://github.com/wangzhecheng/SkyScript. It is a dataset of OSM tags and satellite images. CLIP fine-tuned on that gives good embeddings for text-to-image search as well as image-to-image.