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

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 https://openreview.net/forum?id=7bvWopYY1H) into the embeddings as well.