So the big issue is can be summed up as one present across many domains of ML, but particularly challenging in remote sensing. ML has seen rapid advances in spa

So the big issue is can be summed up as one present across many domains of ML, but particularly challenging in remote sensing. ML has seen rapid advances in spaces where foundational models capture the variance of a domain the latent space of a model. By training on very large dataset, the variance becomes encoded in that latent space; lots of training data implies that the latent space contains meaningful variation. This is what powers things like stable diffusion. A model trained on a very large dataset was connected with another dataset, in latent space. The image model wasn't starting from square 0. It had meaningful variation encoded into it. Enter remotely sensed dataset. They are monumental in scale. So much so that the 804 chips they submitted here is pretty much laughable. Likewise, some parameters of the data have inherent meaning; the pixel dimensions correspond to real world measurements; the bands are specific spectral windows. Its similar but far more than just working with RGB cell phone camera data.