Show HN: My demo for vector embeddings for the Earth's surface
louisquissetlabs.com
louisquissetlabs.com
The embeddings come from globally available Copernicus land cover data.
I don't understand who the target audience is and what this can be used for.
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.
Which copernicus bands were you using? Did you augment the data with DEM info?
https://www.linkedin.com/in/christopher-krapu/overlay/157690...
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.
Some documentation would be helpful.
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, water bodies, etc, overlayed on Google Maps, and allows exporting of the embeddings for lat/lons as cvs. It looks like land features for hexagonal grid areas have been turned into points in a 15 dimensional space, and some sort of nearest-neighbor search is done to return most similar other grid areas within the larger area. It does indeed seem accurate in my area!
I'm not sure what this would be useful for, but I'm assuming urban planning, real estate, agriculture or conservation? I know I'm not the target audience, but more info or ideas would be fascinating.
source: professional urban planning in California
Having worked on these types of problems before the model is doing a pretty great job matching pixels.