Google Unveils Neural Network with “Superhuman” Ability to Determine the Location of Almost Any Image
Next, the team created a database of geolocated images from the Web and used the location data to determine the grid square in which each image was taken. This data set is huge, consisting of 126 million images along with their accompanying Exif location data.
Weyand and co used 91 million of these images to teach a powerful neural network to work out the grid location using only the image itself. Their idea is to input an image into this neural net and get as the output a particular grid location or a set of likely candidates.
They then validated the neural network using the remaining 34 million images in the data set. Finally they tested the network—which they call PlaNet—in a number of different ways to see how well it works.
The results make for interesting reading. To measure the accuracy of their machine, they fed it 2.3 million geotagged images from Flickr to see whether it could correctly determine their location. “PlaNet is able to localize 3.6 percent of the images at street-level accuracy and 10.1 percent at city-level accuracy,” say Weyand and co. What’s more, the machine determines the country of origin in a further 28.4 percent of the photos and the continent in 48.0 percent of them.
