Is an 'ELI5' possible, explaining how the Super Resolution or upscaling of satellite imagery takes place, in broad concept?
My first assumption is that it takes existing datasets of high and low res imagery, covering the same areas, and builds a complex understanding of extrapolation between the two. A sort of reverse-engineering guidebook. It can then be fed low res imagery alone, and refer to the guidebook its built up, in attempt to extrapolate high res output.
"The example data suggests, at X.X% likelihood, that this particular pattern of low res pixels resolves into a high res shape of these particulars : ".
Yes, I think this is part of it -- when the model sees a new low-res image, it compares it to patterns that it has already seen to estimate what that location might look like at high-res.
The other important part is that the model inputs many low-res images (up to 18, i.e. about three months of images) to produce each high-res image. If you were to down-sample an image by 2x via averaging, then offset the image by one pixel to the right and down-sample it, then repeat for two more offsets, then across the four down-sampled images, you should have enough information to reconstruct the original image. We want our ML model to attempt a similar reconstruction but with actual low-res images. The idea breaks down in practice since pixel values from a camera aren't a perfect average of the light reflected from that grid cell, and there are seasonal changes and clouds and other dynamic factors, but with many aligned low-res captures (with sub-pixel offsets) an ML model should still be able to somewhat accurately estimate what the scene looks like at 2x or 4x higher-res (the Satlas map shows a 4x attempt). The current model we've deployed does this far from perfectly, so there are some issues like figuring out where the model might be making a mistake and enabling the model to best make use of the many low-res input images, and we're actively exploring how to improve on these.
My first assumption is that it takes existing datasets of high and low res imagery, covering the same areas, and builds a complex understanding of extrapolation between the two. A sort of reverse-engineering guidebook. It can then be fed low res imagery alone, and refer to the guidebook its built up, in attempt to extrapolate high res output.
"The example data suggests, at X.X% likelihood, that this particular pattern of low res pixels resolves into a high res shape of these particulars : ".