Satelite images are the ground truth OSM is traced on. And the quality of those tracings varies wildly at times, I've had to fix weirdly offset shores that had roads on them placed on the sea on more than one occasion.
If this can be somewhat consistent then it'll probably do better than the average OSM contributor. Something like segmenting houses, roads, bodies of water, comparing against current data and highlighting inconsistencies for correction would be a good start though.
> Something like segmenting houses, roads, bodies of water, comparing against current data and highlighting inconsistencies for correction would be a good start though.
Hi there, I agree that is a valuable usage of a model trained with OSM data.
I didn't have the time to release the code but I am/was doing exactly that to refine the training dataset.
I take the trained model and run it against the ground truth from OSM. Any heavy mismatch between the two almost always result in an useful edit to be made in OSM.
> it'll probably do better than the average OSM contributor
let us reconsider this statement, please. An unexpected and powerful effect of the Openstreetmap project is iterated convergence on ground truth. No person is perfect in contribution, and few people are consistently terrible. Revision and updates, common vision towards accuracy, an appreciation of cooperative contributions.. have astounded the public and humbled critics repeatedly. Not because every key stroke and mouse click is perfect, but because iteration and plural sources have converged in a usable system of software and data.
AI inputs to Openstreetmap are not new, as noted in other comments. The path forward is bright, useful to humans and participatory in the Openstreetmap project.
If this can be somewhat consistent then it'll probably do better than the average OSM contributor. Something like segmenting houses, roads, bodies of water, comparing against current data and highlighting inconsistencies for correction would be a good start though.