Gregory's Server
@zverik That's a very grim outlook and doesn't reflect what I've been seeing. I'm sorry you feel this way.
There's lots of ML at work in the geo domain out there: improving above ground biomass estimations, allowing us to interpret satellite radar imagery for geospatial understanding, cloud removal, improving validation and vandalism detection, spotting outliers and mistakes in the map, better terrain models, better global landcover, and on and on the list goes.
@djh That's a good long list of successful ML applications. I think half of those could be done without ML, but still.
My point is with OpenStreetMap, you're not working in a vacuum, but alongside human mappers, so the net should be at least half as good.
And HOT for some reason chose the one particular application of ML that has been proven to produce bad mapping — it's visible even on their promos.