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I started working on a similar project - my approach was to train a model based on all of the geoguessr contest sets. Geoguessr gives you both the street view

I started working on a similar project - my approach was to train a model based on all of the geoguessr contest sets. Geoguessr gives you both the street view _and_ the correct answer when you guess wrong. They're all hand-curated so there are only so many points that you need to identify (the largest sets are 100k+ points) so the idea would be to run through it thousands of times with wrongs answers but use the correct answer to train the model It's only a matter of time before the game has cheat bots and that problem, much in the same way online chess has that issue

luguenth 6y ago
What would be the advantage to train a model on the whole dataset, you would be running it against later? Wouldn't that overfit the network? And for the cheating part; geoGuessr actually exposes the right answer in their API. You you could just use that to pinpoint the exact location automated.
- ajmurmann 6y ago
  > What would be the advantage to train a model on the whole dataset, you would be running it against later? Wouldn't that overfit the network? The opposite, it would almost be cheating since the model would just have memorized all the correct solutions. You'd usually want to train on a subset of the data and evaluate against the remainder to protect against overfitting. So to that point I agree with your evaluation. But after that you'd use it with real data in the wild for your actual use case. With geogussr you'd know already what all the "real-world" situations are and overfitting wouldn't matter as long as you retrain wherever they add new sets.