[darkmighty]

Since you seem fond of Deep Learning projects, what do you think something like automatic classification of streets (and transportation network in general) from imagery is viable yet? Seems like it would be useful for OpenStreetMap. The corpus of valid classification is tremendous (pretty much all (>95%?) of NA is classified , and the data is readily available.

The subjects themselves don't seem too complex either: lines are small roads, thick lines are major ones, and then there's intersections which semantically interlink them.

[bainsfather]

I was wondering about this also, especially for the case of Humanitarian OpenStreetMap where they map e.g. West Africa and allow you to map without visiting the area (normally not allowed on OSM). The maps can so sparse before we map a region, that any 'AI' would not have to be perfect - it would anyway be a vast improvement on what already exists.

Maybe a good option would be a mapping tool for humans, that traced e.g. a building and then said to the user 'I think this is a building, press Yes to accept'. That would speed up my mapping times by maybe a factor of 5, especially once I got comfortable with the AI being reliable, and could click Yes after just a cursory check.

[darkmighty]

Right, human assist would probably be needed for final verification and unfortunately it's impossible to correctly name the streets (unless everywhere were like Manhattan); number might be doable.

It just seems like a perfect fit for Deep Convolutional neural nets.