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Not Kyle, but another of the team involved in the project. When we built the training model, we used OpenStreetmap data to find locations of ~1,000,000 "things

Not Kyle, but another of the team involved in the project. When we built the training model, we used OpenStreetmap data to find locations of ~1,000,000 "things". A thousand churches, a thousand water towers, a thousand playgrounds, and so on. For each of those locations, we downloaded a satellite image. The neural net was then trained to look for the things that make each one of those things distinct; what makes a playground different than a church? It could be patterns, it could be colors, it could be any number of things. (For more precise details, you'll need to talk to Aman or Kyle.) It compares lots of things to lots of things, makes some guesses, and then sees whether those guesses help it correctly determine what we told it was in each tile. Once the model is trained, it's identified the 1024 "features" that are most significant in correctly distinguishing types of things from each other. We then run every tile of a geographical region through that feature determiner, which converts each tile into a point in our 1024 dimensional space. The search function then identifies a tile, looks up its location, and finds the 100 things closest to it within the 1024 dimensional space. So, TL;dr: It's not looking for colors, it's looking for computable features, which may or may not be color-specific. (Actually, they're highly non-color-specific: the training model randomly "wiggles" the color to makes sure that it doesn't get too tied to a very precise color.)

dockd 10y ago
In my experience in the USA, OpenStreetMap data that comes from TIGER (?) imports, like heliports, are close but wrong. Playgrounds and watertowers are usually accurate because someone added them by hand. Churches are mixed. Other items are confusing; a hospital is typically a single point, but they tend to be some of the largest buildings in small towns. Any thoughts on how that affects your model?
- maxerickson 10y ago
  Most of the messy point features in the US were imported from GNIS. TIGER is the primary source for the messy line features. Sticking to features modeled as areas would generally avoid the spatial accuracy problems with GNIS.
- workergnome 10y ago
  We found the same—using OSM data that just had points was problematic, both because of accuracy and because it tended to often be the front door, not the centroid of the object. I believe we limited our model generation to selecting places that had outlines, and computed the centroid of that outline. One of the benefits of our technique is that we didn't need to be comprehensive—we can throw out lots of places and still have enough to be useful for the model.
maxerickson 10y ago
Any plans to take imagery with suitable licensing and produce a list of items that seem to be missing in OSM? (Google prohibits feature extraction in their TOS and claims copyright, so OSM can't use information derived from their imagery)
danso 10y ago
Thanks for the explanation...yeah, as I said, you're talking to a layperson :). I just realized that it was possible that the training was done on images where the color scale was changed, so thinking about it in terms of "well blue patches are so easy to see" is obviously wrong.

dockd 10y ago

In my experience in the USA, OpenStreetMap data that comes from TIGER (?) imports, like heliports, are close but wrong. Playgrounds and watertowers are usually accurate because someone added them by hand. Churches are mixed. Other items are confusing; a hospital is typically a single point, but they tend to be some of the largest buildings in small towns. Any thoughts on how that affects your model?

maxerickson 10y ago
Most of the messy point features in the US were imported from GNIS. TIGER is the primary source for the messy line features. Sticking to features modeled as areas would generally avoid the spatial accuracy problems with GNIS.
workergnome 10y ago
We found the same—using OSM data that just had points was problematic, both because of accuracy and because it tended to often be the front door, not the centroid of the object. I believe we limited our model generation to selecting places that had outlines, and computed the centroid of that outline. One of the benefits of our technique is that we didn't need to be comprehensive—we can throw out lots of places and still have enough to be useful for the model.

maxerickson 10y ago

Any plans to take imagery with suitable licensing and produce a list of items that seem to be missing in OSM? (Google prohibits feature extraction in their TOS and claims copyright, so OSM can't use information derived from their imagery)

danso 10y ago

Thanks for the explanation...yeah, as I said, you're talking to a layperson :). I just realized that it was possible that the training was done on images where the color scale was changed, so thinking about it in terms of "well blue patches are so easy to see" is obviously wrong.