This tool looks very interesting, and seems to work well, but being utterly unfamiliar with geospatial vector embeddings, their purpose or use, I had no idea what I was looking at, or why. It seems to show areas of similarity, within a radius of a central query location, with regard to (perhaps) vegetation cover (e.g., forests, grasslands, wetlands), artificial surfaces (e.g., urban areas, roads), agricultural areas,…
Show HN: My demo for vector embeddings for the Earth's surface
31–40 of 42 posts
Re: Show HN: My demo for vector embeddings for the Earth's surface
#32Earlier quoted context omitted.
It's due to the fact that they used satellite imagery to create the embeddings. The map is just for visualization. They probably used 5 or more bands of the satellite data which means each pixel is going to be slightly different due to things like depth, amount of silt in the water, amount of plankton.... Having worked on these types of problems before the model is doing a pretty great job matching pixels.
Thanks! And you are giving it too much credit here - it's just trained on one-hot encoded land cover (24 classes) from Copernicus. Using imagery directly would be # 2 on my list of to-dos after including elevation in the input data.
Re: Show HN: My demo for vector embeddings for the Earth's surface
#33This tool looks very interesting, and seems to work well, but being utterly unfamiliar with geospatial vector embeddings, their purpose or use, I had no idea what I was looking at, or why. It seems to show areas of similarity, within a radius of a central query location, with regard to (perhaps) vegetation cover (e.g., forests, grasslands, wetlands), artificial surfaces (e.g., urban areas, roads), agricultural areas,…
You pretty much hit the nail on the head. The application areas you mentioned are the same as the ones that I had in mind when developing this.
source: professional urban planning in California
Re: Show HN: My demo for vector embeddings for the Earth's surface
#34Earlier quoted context omitted.
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 sim…
Re: Show HN: My demo for vector embeddings for the Earth's surface
#35Re: Show HN: My demo for vector embeddings for the Earth's surface
#36Moved the center to SF and I've been sitting, watching the spinner. Some documentation would be helpful.
Re: Show HN: My demo for vector embeddings for the Earth's surface
#37Moved the center to SF and I've been sitting, watching the spinner. Some documentation would be helpful.
I've seen the same thing, querying SF hangs for some reason. And so does Cascais in Portugal. It works in San Mateo and Lisbon though
Re: Show HN: My demo for vector embeddings for the Earth's surface
#38I've had to build out some version of a geospatial vector embedding / latent variable dataset for at least 4 separate projects now. Come see the viewer I've built on top of it! The embeddings come from globally available Copernicus land cover data.
Having originally come from the world of geointelligence, let me tell you this is not an easy problem to solve. For rural land use, this is probably fairly reliable, but depending on the granularity of change detection you want, cities are often building new neighborhoods in the span of months, large construction projects finish, human movement happens more in the span of hours or even minutes, and that's just for land. If you want maritime tracking, you need nearly continuous updates. We managed to do it for the Navy, but the infrastructure required for this is immense, much of the sensor technology is classified and not even available for commercial use, and the resource requirements not remotely practical for a personal side project.
Of course, military intelligence is primarily trying to track the land use of other militaries, especially in active theaters of operations, and that changes even more frequently than regular places where people aren't constantly erecting and moving temporary headquarters, living under camouflage cover, and blowing up existing infrastructure.
I guess you're doing this for peacetime domestic real estate, like neighborhood X in city Y is similarity ranked against neighborhood U in city V? Are you incorporating pricing and demographic data or just land use? It seems to me like neighbors make the neighborhood, as much or more than qualities of the land. Along with things like usability of the sidewalks, responsiveness and level of disrepair of the roads, crime rates, level of visible homelessness, air quality, vehicular traffic congestion.
I don't want to shit on the approach too much. Usefulness is determined by the results you get, but given the heterogeneity of the data here, some of it ordinal, some of it nominal, discrete versus continuous, irreconciability of scaling and dimensional analysis, not necessarily coming from similar distributions if you tried to just z-score it all, I can think of ways using pure numerical voodoo to put them all into the same vector space, but the statistical validity of doing this is dubious at best.
Re: Show HN: My demo for vector embeddings for the Earth's surface
#39Earlier quoted context omitted.
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 sim…