No the tech doesn’t work like that AFAIK. The most common use case is exactly localization (think “HD maps” for autonomous cars).

It almost 1-1 data correlation, n-phone Pokémon go scans of a location helping a drone locate itself in the same location in correlation with Maxar’s satellite data.

There maybe some hyper corner case uses. Maybe the billion scans in New York City help them generalize across different phone lenses characteristics, but phone and drone lenses are so different.

Would love to hear some specifics if I’m wrong here.

> It almost 1-1 data correlation, n-phone Pokémon go scans of a location helping a drone locate itself in the same location in correlation with Maxar’s satellite data.

The headline, which I do understand is in question, talks about training, not using the scans as a database. It is likely that you are right that the scans are not being used to provide localization data, but that is also not what the headline is pointing to.

The headline specifically speaks to using the scans for training. While I do not have any inside baseball, the problem space is often solved using neural nets and other machine learning algorithms. On the surface it seems likely that they would benefit from training data that doesn't necessarily need to be from where the conflict is actually taking place. A base world model, for example, can be developed from data collected anywhere in the world. Its is not an entirely different universe when you step into another country.

But you are suggesting that the algorithms used are entirely classical (i.e. no AI/ML)?

You are creating a 3D model when you scan using Pokémon Go. Difference in lenses doesn't matter, that only matters for the scanning step.