This is a very cool visualization, and I will continue to play around and explore various parts of the country. One thing I noticed immediately, due to where I live, was an interesting consequence of the algorithm. According to the model, there are several very large buildings nearby (see the outskirts of Forest Grove in the picture).
There is one cluster in the lower left (near Ritchey Rd) and another in the upper right near Schefflin. These are not really buildings, they are hoop-houses. Structures made of only plastic sheets and a few metal pipes. You could argue this is nit-picking, and I’m fine with that characterization. But I’d love to press the issue and ask what would it take to train a neural network better… so it knows the difference between what you and I would call a building, and what merely looks like a building from space.
To be more clear, here is a picture of the Ritchey Rd area:
And a full-zoom of the satellite image, corresponding to the top left (northwest) portion of the nursery (the NYT article doesn’t zoom in from here):
Clearly these are structures, but what would be the next step in developing the algorithm to know they aren’t actually buildings (or do you define them as such?). Certainly including some nurseries in the training set would be a first step.