5 ms·

You could improve the accuracy by using the timestamp of when the photo was taken & looking at the amount of daylight in the photo.

markovbling 11y ago
Interesting idea!
asciiiiii 11y ago
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.
- astrange 11y ago
  For known cameras like iPhone, that shouldn't hurt since you can read the exposure time out of the EXIF.
- Veratyr 11y ago
  If you can also get accurate white balance, you may be able to get useful information from the colour of the sky as an alternative.
est 11y ago
> the amount of daylight wouldn't that be much effected by weather conditions and air quality ?
- stephenboyd 11y ago
  That would actually improve the accuracy since the image can be compared to historical weather records.
jononor 11y ago
Possibly also length of shadows? (sounds tricky though)
- joshfraser 11y ago
  You'd let the neural network do the heavy lifting there
jeswin 11y ago
This could be a dumb question, but could it be possible that the Neural Network would have already identified this correlation?
- stephenboyd 11y ago
  It's a great question, but the only information about the photos they used was the visual data from the pixels and what albums they were in.
GrantS 11y ago
You would probably be interested in the computer vision work of professor Nathan Jacobs over the last decade: http://cs.uky.edu/~jacobs/ 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.
stephenboyd 11y ago
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.

markovbling 11y ago

Interesting idea!

asciiiiii 11y ago

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.

astrange 11y ago
For known cameras like iPhone, that shouldn't hurt since you can read the exposure time out of the EXIF.
Veratyr 11y ago
If you can also get accurate white balance, you may be able to get useful information from the colour of the sky as an alternative.

est 11y ago

> the amount of daylight wouldn't that be much effected by weather conditions and air quality ?

stephenboyd 11y ago
That would actually improve the accuracy since the image can be compared to historical weather records.

jononor 11y ago

Possibly also length of shadows? (sounds tricky though)

joshfraser 11y ago
You'd let the neural network do the heavy lifting there

jeswin 11y ago

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

stephenboyd 11y ago
It's a great question, but the only information about the photos they used was the visual data from the pixels and what albums they were in.

GrantS 11y ago

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

stephenboyd 11y ago

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.