GIS Learning

The examples on the left are the query photos. In response, PlaNet will output a probability distribution on the map. In these three examples, the Eiffel Tower (a) is confidently assigned to Paris, the model believes that the fjord photo (b) could have been taken in either New Zealand or Norway. For the beach photo (c), PlaNet assigns the highest probability to southern California (correct), but some probability mass is also assigned to places with similar beaches, like Mexico and the Mediterranean. The authors use a model with a much lower spatial resolution than the full model for visualization purposes. Source: Weyand, Kostrikov, & Philbin, 2016.The examples on the left are the query photos. In response, PlaNet will output a probability distribution on the map. In these three examples, the Eiffel Tower (a) is confidently assigned to Paris, the model believes that the fjord photo (b) could have been taken in either New Zealand or Norway. For the beach photo (c), PlaNet assigns the highest probability to southern California (correct), but some probability mass is also assigned to places with similar beaches, like Mexico and the Mediterranean. The authors use a model with a much lower spatial resolution than the full model for visualization purposes. Source: Weyand, Kostrikov, & Philbin, 2016.

Google’s PlaNet: Geolocating Photos Using Artificial Intelligence

GIS Learning

Google and researchers at the Rheinisch­Westfälische Technische Hochschule Aachen University have developed an artificial intelligence system capable of identifying locations more consistently accurately than a human is able to do.