[lmeyerov]

I'm not sure what answer you are looking for / what alternative you are suggesting?

OSM is the biggest community effort - NGO, volunteer, corporate, etc - to solve data quality in GIS. The participants do everything to improve quality from individuals walking around with GPS devices to companies launching low earth orbit satellites into space and self-driving cars in the ground with AI error detection.

There is a more corporate and afaict anti-google effort more recently by tomtom and google competitors (Microsoft, meta) called Overture, which seems to be attempting a more closed and big corporate governed fork & ecosystem replacement of OSM, even if they phrase it as complementary. Your questioning of OSM-as-misinformation seems interesting in the comparative context of alternatives like Google Maps, Apple Maps, and Overture.

[wolverine876]

> I'm not sure what answer you are looking for / what alternative you are suggesting?

None of the above. People might just be curious and interested!

Thanks for sharing all that. I didn't know much about the quality. Much crowd-sourced data has accuracy problems.

[mikeocool]

It is an interesting question. I've worked with a fair bit of geo-data, and found pretty much any sufficiently large geo-dataset, regardless of whether it's crowd-sourced or purchased from a commercial provider tends to have a fair number data accuracy issues.

If you spend any time on either OpenStreetMap or Google Maps in the rural US, and you are very likely to find both missing streets, and streets that have been completely hallucinated out of nothing -- mostly because they were both originally sourced from the US CENSUS TIGER data -- a US govt data set that tends to be full of errors.

OpenStreetMap contributors of often improve these issues over time (though it largely depends on if the area you care about has some sufficiently dedicated mapper).

[resoluteteeth]

Google maps also isn't always accurate. There's no source of maps that's guaranteed to be 100% accurate all the time.

[wolverine876]

> 100% ... all the time

Because nothing meets that standard, it's not an applicable measure of quality. Also, it tends to lead to binary thinking: 100% or null.

But we live in a world of less than 100%, and there is a lot of variation: Five nines, 80%, datasets with just a few valuable records, etc. That's where the real-life questions are.