I genuinely can't work out what part of this video you think is US-specific. America is far from the only country in the world that has municipality logos on infrastructure, house numbers, or OSM coverage.
There's certainly regions of the world where doing this would be much more challenging (e.g., Central Africa, China, rural India), but the stuff he covered in the video is going to be extremely helpful in the vast majority of cases.
One thing I noticed that doesn't generalize is exactly those house numbers. Depending on house numbering scheme, a house number may effectively be useless. Here in Israel house numbers are just sequential and most streets aren't that long, so what are you going to do with a number like "17" which appears in almost every street in the country?
> so what are you going to do with a number like "17" which appears in almost every street in the country?
Exactly the same thing that Rainbolt did in this video: cut down the amount of work you have to do on each street from checking dozens or potentially hundreds of photos/angles to just 1-3.
Of course if you've only narrowed the streets down to 20,000 candidates instead of 20 of them, that doesn't get you straight to the answer, but it's still a massive proportional improvement.
But the lesson being presented here is to use data that's available to you in the photograph. Maybe you don't have any street numbers (or any particularly useful ones) visible, but you can see the sun at the end of the road and therefore know that it's running directly East-West. That filters out tons of roads. Maybe you can see that houses are only on one side and a river is on the other, you can use that as well. In the video he mentions similar constraints with regards to local parks as being other options for this kind of search narrowing.
The point of that portion of the video isn't "hope the house number is really weird lmao", but to extract any geographical information out of the image and then query that with open mapping databases. House numbers are just one of the most common ones, and while they're typically not quite as powerful as was shown in this video, they're very often going to help dramatically.
Same here, and to that I don't think I know of any municipality logos, pretty sure that is not a thing. And all license plates in the country are going to be the same.
Not exactly US-specific, but Streetview isn't available everywhere. How would he have found the exact ___location without it? Would be quite interesting!
There's a few open data projects that provide similar features to Streetview and can very occasionally be useful in regions without coverage. But yes, typically outside of Streetview coverage you'll be relying on satellite photography, which makes things far more difficult. But that doesn't exactly make the advice here any worse, it just means that the problem you're trying to solve is fundamentally more challenging, so even good techniques might not be able to get you to the solution.
I haven't used his method (or tools) for finding locations. On his video though he does mention Austria/Vienna for museums, etc (on his screenshare), so I assume (assumption1) that if it shares data with OsmAnd (and others) it would be very useful for (anywhere) where internet is prevalent (Americas, Europe, Oceania, most of Asia).
> The purpose of the Korean Land Survey Act was to prevent anyone who is threat to national security from stealing the country’s maps during the post-war period. ROK government enacted the law in 1961 and it concerned the establishment and management of spatial data — it has subsequently been amended in 2009. The Article 16 of the act states strict regulation on taking maps, photos, the results of a survey, or any land surveillance data abroad, because of the likelihood that it could harm South Korean national security interests.
