They also do some object recognition, which is useful if you're an electric infrastructure nut. It spotted some solar fields in Shanghai which I've never heard of before -- a look at the same coordinates (30.753, 121.392) on Google sure shows the expected blue.
We report the accuracy of the data at [1]; the Satlas project is quite new and we're aiming to improve accuracy as well as add more categories over time.
We expect the geospatial data will be useful for certain applications, but I agree that the upscaled super-resolution output has more limited uses, especially in its current state outside the US since it is trained using NAIP imagery that is only available in the continental US. We're exploring methods to quantify and improve the accuracy of the upscaled imagery.
Note that the model weights, training data, and generated geospatial data can all be downloaded at [2].
[1] https://github.com/allenai/satlas/blob/main/DataValidationRe...
Also, all of the models input three or four images of the same location (captured within a few months), with max temporal pooling used at intermediate layers to enable model to synthesize information across the images. This helps a lot, definitely when one image has a section obscured by clouds (so model can use the other images instead), and maybe also when different images provide different information (e.g. shadows going in different directions due to slightly different times of day).