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I've played around with Overpass and GPT a bit. Here are a few examples that I think demonstrate the potential:

Find all buildings that straddle the boundary between Glendale and Burbank in California:

https://twitter.com/lemonodor/status/1636849040548675584

Finding the suspected origin of an explosion, by using the time from seeing the explosion on video to the time the sound of the explosion reaches the microphone:

https://twitter.com/lemonodor/status/1636859983223734273

Someone on twitter asked "Is anyone aware of any 4 way stop intersections in Australia?"

https://twitter.com/lemonodor/status/1516239321094713346

Finding examples of airport runways that cross highways:

https://twitter.com/lemonodor/status/1709598048459132976

Finding banks that might be at risk of robbery:

https://twitter.com/lemonodor/status/1716153200750051332



Bellingcat's Bellingcat OpenStreetMap search is a tool that wraps Overpass Turbo in an easier-to-use interface for the specific task of geolocation based on image and videos: https://osm-search.bellingcat.com


Here’s a video showing the sort of geolocation task the Bellingcat tool is intended for: https://youtu.be/GqKNKQ02pjY?si=DyAB3YZzl3gJUzT9


This is awesome, but a lot of the queries I've tried to make seem to fail on OSM's data itself, which is sad. For instance, "coffee shops within a certain distance of fast EV chargers" would be really valuable, but the underlying data just doesn't have that EV data. So it's cool for a lot of stuff, but mostly explicitly streets; the other kinds of nodes all exist but aren't as well fleshed out as they could be (totally understandable).


That’s because we’re all cyclists ;)




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