I'm a regular GeoGuessr player and I use the meta stuff mainly for getting quickly out of boring locations without losing too much points.
For example South America and Africa are huge and it's really no fun getting dumped somewhere far from any civilization and having to drive hours to get any real life geographical hints.
It’s a bit like asking if intentionally fouling to force free-throws and a possible turnover late in a basketball game is against the spirit of the rules. Maybe yes as they were originally conceived when it was a recreational activity years ago, but at the modern competitive elite level it’s very much part of the game.
This stuff must get outdated very quickly. Google could very well upload an entire new set of photos for Mongolian roads tomorrow. How do Geoguessers keep on top of that? Is there a central repo/db where they store the dates that each country, or even potentially province or town, get updated by google.
Im imagining some kind of RSS feed that Geoguessers all subscribe to which alerts them of a country update and they all scramble to catalog the new meta.
> This stuff must get outdated very quickly. Google could very well upload an entire new set of photos for Mongolian roads tomorrow. How do Geoguessers keep on top of that?
It's like a metaphor for life: you tend your garden, then the storms come and level everything, and then you tend a new garden from scratch. No human labor is everlasting—it's why the nomads invented yurts.
Ah is that how Geoguessers work? process of elimination through elements within a LIMITED dataset. Still impressive nonetheless, but I was wondering if I took a picture from my rooftop is Kenya how they would be able to guess
Only street view is used in Geoguesser (thus a slightly more limited dataset). AFAIK Individual photos (even ones that can make up a janky sort of street view) aren't included
It took me a while to realize that first section was about how to guess based off the Google Street View car's characteristics (tire, roof rack). I didn't know people were even allowed to use that as part of the game. I guess I'm only watching some specific people like rainbolt, who seems to use the terrain and other city landmarks (stop signs, poles along highways, etc) for his guesses.
I'm guessing for Google Street View Car stuff that's probably a differentiator if you're super competitive at this.
Rainbolt also uses car meta in specific scénarios (Kenya is obviously a big one), it's just that it's obviously less impressive so it doesn't make it into viral videos. If you watch streams it's quite evident he's very aware of car meta.
If author is reading, they can also export the docs to a html document, which google will host. This is much better for sharing, as it loads faster, doesn't get added to users google docs and doesn't have a limit on concurrent viewers.
There are some really cool geoguessr metas that aren't as much about memorizing the street view cars that I love. (Although, a few car metas are interesting such as the handful of African countries that required a security follow car to tail the Google car, or the “Kenya snorkel” on the more off road capable street view vehicle for Kenya.) One of these is that there is a geostationary satellite just south of Texas that most US satellite dishes will point to, so if you can find a house with a satellite dish on it then you can combine its angle with your compass and get a rough sense of the US region you are in.
All the time and effort spent optimizing a corner of the rules of a specific dataset of a game.
It strikes me as wasteful in the same vein as Bitcoin mining, but more than that, as running contrary to what learning is supposed to be.
Signifier replacing the signified, metrics standing in for reality.
It's interesting though, we hear so much about AI, this is a sport that could have been invented for AIs to do - judgement tasks with quantifiable results and quick repetition.
40 comments
[ 75.0 ms ] story [ 283 ms ] threadI got nothing on the hardcore geoguessers
Looking at the car, the picture quality or other image artifacts is a very useful tactic that everyone uses.
https://www.sirlin.net/ptw
"David Airline's Playing to Win has been the competitive gamer's bible since the year 2000."
For example South America and Africa are huge and it's really no fun getting dumped somewhere far from any civilization and having to drive hours to get any real life geographical hints.
Im imagining some kind of RSS feed that Geoguessers all subscribe to which alerts them of a country update and they all scramble to catalog the new meta.
Refer to the Meta meta document.
I'm guessing for Google Street View Car stuff that's probably a differentiator if you're super competitive at this.
All the time and effort spent optimizing a corner of the rules of a specific dataset of a game.
It strikes me as wasteful in the same vein as Bitcoin mining, but more than that, as running contrary to what learning is supposed to be.
Signifier replacing the signified, metrics standing in for reality.
It's interesting though, we hear so much about AI, this is a sport that could have been invented for AIs to do - judgement tasks with quantifiable results and quick repetition.