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Comment by fnordpiglet | Hacker News Reader

Can you explain what I’m looking at? I don’t know how to interpret the hex tiles :-)

Sure! The basic idea is that each hexagon is a discrete unit of space for which I obtain a vector embedding. This vector is supposed to represent a sort of data-based summary of that location, obtained in this case using deep learning.

When you put the search on a hex, it looks up the vector for that hex and then performs a similarity search on all other vectors within the circle and shows the ones which are most similar in terms of land cover. The dependence on land cover / land use data is just because that was easy to get.

As other folks have pointed out here, raw satellite imagery is also a potential input source for this. I'm playing around with other sources and really want to integrate something like GeoVex (https://openreview.net/forum?id=7bvWopYY1H) into the embeddings as well.

Would it provide useful/interesting results if the similarity search was global? E.g. find me neighborhoods in London most similar to this one in Chicago?

I'm pretty sure I'm not the intended audience but I also have no idea what this is used for. Surveying? Real estate tycoons? Oil & gas exploration?

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.

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.

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.

Great question. A legend or brief description of the underlying logic / heuristic would be helpful.

The heuristic is likely the result of an ML algorithm, so the underlying logic may not make much sense to us.

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