I'm a GeoGuessr fan, and like the author, experienced a bit of a GeoGuessr renaissance when I had a baby. I guess it's the sort of slow, quiet game that lends

I'm a GeoGuessr fan, and like the author, experienced a bit of a GeoGuessr renaissance when I had a baby. I guess it's the sort of slow, quiet game that lends itself to being played between baby care tasks. In any case, the criteria discussed here is the sort of thing that all too often makes me skeptical of deep learning results. There are the things that I consider "real" discriminators when playing the game - architectural styles, building materials, road quality, driving direction, alphabets/languages on signage, flags, flora, topography, makes/types of vehicles, skin color and clothing style of humans, etc. Then there are the "artificial" things - glimpses at the street view vehicle, copyright notices, image quality, knowledge of which countries do or don't have Google Street View coverage, etc. Obviously by taking what I'm calling the "artificial" criteria into account a deep learning approach would score better today than if only the "real" criteria were considered. But I feel like if tomorrow, GeoGuessr swapped Apple Maps Look Around or Bing Maps StreetSide in place of Google Street View (or if Google released vastly updated imagery), the deep learning approach would fall apart, but humans who have built knowledge of the "real" features to look for would continue to do just about as well.