It's a way to encode land to make predictions of it. E.g. is the land arable, is it rural, how similar is it to X, etc. Embeddings help encode data in formats more usable by ML models.
Sure, I get that part -- but then how do people use the predictions?
The embeddings are used by algorithms, not people, generally. You could ask something like "what's the most similar place to X within Y", and it would using the embeddings (which cover a variety of facts) to calculate answer. An embedding is an N dimensional vector (where the dimensions may or may not be meaningful to us), and similarity can be implemented by looking at the similarity between vectors.
Yup, and while the similarity search is perhaps the most visually appealing way to work with it, the real use (in my opinion) is in providing generic sets of geospatial features which are reusable across applications. I've built out versions of H3-referenced feature sets at each of the jobs I've had over the last 10 years.
The question was: in what context do people need to answer a question like "which geographical points are close to X and similar to X"?
I don't understand who the target audience is and what this can be used for.
The original idea came from something I saw at work - we needed a way to build generic feature sets representing something about real estate, but beyond the data we had on prices, floors, and other house-specific details.
My guess is this site is simply a way to explore the embeddings. People make similar data visualization tools for word embeddings, so that's what I assumed this was.