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.
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.