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Text Content

GEOLOCATION ESTIMATION

Login Sign Up

CREATE AN ACCOUNT FOR GEOLOCATION ESTIMATION

×

To get an account, please contact matthias.springstein@tib.eu and briefly
describe your use case and we will create an account for you.

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Instructions


Select a photo under "Image Selection" and estimate its geolocation by clicking
on the world map.

Under "Image Selection" you can either choose an image from a predefined
benchmark test set or upload your own photo. Your performance compared to our
deep learning approach and the scene classification (probabilities for indoor,
nature, urban) results are shown in the sections "Statistics" and "Scene
Classification", respectively.

After you have clicked on "Guess Location" the results are calculated and
presented. The world map is overlaid with a heat map, where the regions colored
in red represent the most likely locations of the photo at hand. In addition,
the photo parts that contributed at most to the decision are visualised (blue =
“less important” to red = “very important”).

Image Selection Choose Random

 * Open (48/48)
 * Annotated (0/48)

©__maurice CC-BY-NC 2.0

No annotated photos.


Guess Location
Guess Location
+−


Leaflet | © OpenStreetMap contributors
Marker: User Model Ground truth EXIF (if available)
Result

Distance to ground truth or EXIF location:

You:
Model:
Scene Classification
 * Probability indoor: ---
 * Probability nature: ---
 * Probability urban: ---
 * Predicted scene: ---

Statistics Reset
 * Annotated images: 0
 * Rate of sucess: ---
 * Your mean error: ---
 * Model's mean error: ---

Reference

For details on the deep learning approach please check our publication:

Müller-Budack E., Pustu-Iren K., Ewerth R. (2018) Geolocation Estimation of
Photos Using a Hierarchical Model and Scene Classification. In: Ferrari V.,
Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV
2018. Lecture Notes in Computer Science, vol 11216. Springer, Cham
This work is financially supported by the German Research Foundation (DFG
project number 388420599).