Gregory's Server
@djh What changed is, couple years ago I assumed the tech might get better, with the proper pre- and post-processing, increased power, and applied geospatial knowledge.
Now we see it's all dazzle, MVP with no VP in sight. No people with geo experience do ML, it's all twiddling settings of models invented last century. Because people are not profitable, hype is.
And there are no two identical buildings out there, stats won't do. Fixing ML output would mean rewriting detection with heuristics.
@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.