Re:Show HN: My demo for vector embeddings for the Earth's su...
by ckrapu on Sunday September 17, 2023 @06:36PM
(#37549997)
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.
Re:Show HN: My demo for vector embeddings for the Earth's su...
by jebarker on Sunday September 17, 2023 @09:43PM
(#37551251)
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?
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.