This is only about using the pixels, not anything from the exif data. The only additional information was used in a later iteration of the model, taking advantage of knowing that photos in the same album were more likely to be taken in the same places. Also, there was a section in their paper about comparing PlaNet to human players at the GeoGuessr.com game, specifying that both had the exact same information available; there's no time information with GeoGuessr.

I'm sure any serious organization actually putting together a product to locate images that have time—but not location—data would try to take advantage of the outdoor color temperature, lighting, global weather records, text recognition, and all that kind of stuff. This was a more focused research project.

This could be a dumb question, but could it be possible that the Neural Network would have already identified this correlation?

You would probably be interested in the computer vision work of professor Nathan Jacobs over the last decade: http://cs.uky.edu/~jacobs/

He has done some amazing work extracting geo-location and scene structure based on daylight patterns, shadows cast by clouds, rainbows, etc in images and image sequences.

Will this really work? Cameras are by design able to accurately control the rate (and duration) of light they let in to create an image.

> the amount of daylight

wouldn't that be much effected by weather conditions and air quality ?

Possibly also length of shadows? (sounds tricky though)

Interesting idea!