New algorithm for inferring road surface tags

Hi all! This is my first forum post, so apologize if this the wrong category.

I wanted to bring attention to an open source project I’ve been working on, which is a road surface-type classifier algorithm for OSM. Currently it is trained on USGS NAIP imagery (it’s the only tiled imagery source that allows offline processing), and so it is limited to the United States. For the limited resolution imagery (stuck with ~2m GSD imagery using the tiled source), it does quite well: >90% accuracy at classifying paved vs unpaved roads, and >80% accuracy when splitting between asphalt, concrete, bricks, and unpaved!

The motivation behind the project is since the TIGER import in the US is largely unverified, I’m hoping to add surface tags to help open source routers make more-informed decisions. I find that this overall lack of knowledge of which roads are paved / unpaved is prohibitive in my area (rural CO).

I’m also currently starting work on a JOSM plugin that can leverage this model to allow quick classification of the road surface types over a broad area, allowing the user to quickly accept / reject the algo’s result and correct the label.

I’m curious on overall feedback and reception of an idea like this, since I’m happy to expand the concept to tagging other things (number of lanes maybe)? I’m also interested in access better imagery sources if they are available in order to get higher resolution data and/or global coverage.

I’m also interested in any feedback regarding how this project can best support its goal for OSM, adding road surface tags efficiently. Currently I’m working on a JOSM plugin but curious if there were any other ideas.

Project link is here:

Thanks all!


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