The issue is it’s going to be trained only on places people go. My understanding is that most geolocating systems are more focused on the uncommon or less traveled locations. Maybe I’m wrong, but I guess they’re probably tested it out before announcing it.
You’d be surprised how a pokemon go player will open the app in the middle of the wilderness, even with one questionable reception bar, just to see if there’s a secret rare pokemon that lives there. Bonus if you get a postcard from a pokestop someone submitted, because that trail mile marker is “special.” You can keep it for memories, or send it to a friend you’ve never spoken to, for bragging rights. It’s also not unheard of, for people to crawl through backroads in their car, since the app won’t let catch pokemon or count km’s, if you go more than ±15 mph.
AI delivery bots maybe? It’s basically an aggregate of “here’s where it’s possible/common to walk” so it’s not useful for driving/flying AI. Also useful for marketing, knowing where foot traffic is.
The article says they’re treating it as a Large Geospatial Model (like a Large Language Model), so it seems like you could use that as a predictive way to navigate between two points. With an LLM it spits out phrases based on context. The LGM would return paths based on context.
What do we need this for? Not clear what it does
Seems to be a tool for taking an image and Geo-locating it, even in cases where other sources of data like Googles street map cars are insufficient.
The issue is it’s going to be trained only on places people go. My understanding is that most geolocating systems are more focused on the uncommon or less traveled locations. Maybe I’m wrong, but I guess they’re probably tested it out before announcing it.
Maybe they will start/already have putting pokemon in locations that they need data for to make a more comprehensive map.
You’d be surprised how a pokemon go player will open the app in the middle of the wilderness, even with one questionable reception bar, just to see if there’s a secret rare pokemon that lives there. Bonus if you get a postcard from a pokestop someone submitted, because that trail mile marker is “special.” You can keep it for memories, or send it to a friend you’ve never spoken to, for bragging rights. It’s also not unheard of, for people to crawl through backroads in their car, since the app won’t let catch pokemon or count km’s, if you go more than ±15 mph.
Seems questionably reliable as an independent system. Maybe could help refine more traditional systems? Not sure I understand a use case for this.
It definitely has applications in defense/Intel spaces. Knowing where a photo was taken can be really useful info.
AI delivery bots maybe? It’s basically an aggregate of “here’s where it’s possible/common to walk” so it’s not useful for driving/flying AI. Also useful for marketing, knowing where foot traffic is.
The article says they’re treating it as a Large Geospatial Model (like a Large Language Model), so it seems like you could use that as a predictive way to navigate between two points. With an LLM it spits out phrases based on context. The LGM would return paths based on context.