This is such a weird headline and dataset. It's not a very large model, esp for geospatial. And the data set is microscopic, not even 1k image tiles.
A typical geospatial UNET would be trained on any where from 10x to 100x this much data.
This is more like a toy dataset I would give an intern to play on. But to be clear, one would need much much much more data do do something interesting on. Likewise, there are a lot of data filtering and data processing considerations that come into play with satellites like clouds, ascension or descenion, averaging to try and get fewer clouds. Satellite and all remote sensing ML is tricky stuff.
Vanilla UNET is around 7-8M parameters, this is 100M(?) so the model itself is an order of magnitude larger. There are larger models though as pointed out in the other Hacker News thread.
The fine-tuning datasets are much smaller, but that's the point - they don't need to be large, because of the foundation model underneath.
Yeah, I'm surprised they released the demo about multi-temporal crop prediction. Their accuracy is, frankly, pretty terrible. It's basically what I managed the first time I tried to run a classifier against the CDL dataset across years.
It will be the largest geospatial foundation model on Hugging Face and the first-ever open-source AI foundation model built in collaboration with NASA.
Perhaps it’s a sad statement that this is the largest GIS model on HF etc, but at least it’s out there. I would love to see more and better and larger and less entangled with IBM or other megacorps out there.
A typical geospatial UNET would be trained on any where from 10x to 100x this much data.
This is more like a toy dataset I would give an intern to play on. But to be clear, one would need much much much more data do do something interesting on. Likewise, there are a lot of data filtering and data processing considerations that come into play with satellites like clouds, ascension or descenion, averaging to try and get fewer clouds. Satellite and all remote sensing ML is tricky stuff.