%0 Conference Paper %1 weyand2016planet %A Weyand, Tobias %A Kostrikov, Ilya %A Philbin, James %B Computer Vision -- ECCV 2016 %C Cham %D 2016 %E Leibe, Bastian %E Matas, Jiri %E Sebe, Nicu %E Welling, Max %I Springer International Publishing %K classification cnn deep deeplearning geo geolocation image learning machine ml network neural planet %P 37--55 %R 10.1007/978-3-319-46484-8_3 %T PlaNet - Photo Geolocation with Convolutional Neural Networks %U https://link.springer.com/chapter/10.1007/978-3-319-46484-8_3 %X Is it possible to determine the location of a photo from just its pixels? While the general problem seems exceptionally difficult, photos often contain cues such as landmarks, weather patterns, vegetation, road markings, or architectural details, which in combination allow to infer where the photo was taken. Previously, this problem has been approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, this model achieves a 50 \% performance improvement over the single-image model. %@ 978-3-319-46484-8

@inproceedings{weyand2016planet, abstract = {Is it possible to determine the location of a photo from just its pixels? While the general problem seems exceptionally difficult, photos often contain cues such as landmarks, weather patterns, vegetation, road markings, or architectural details, which in combination allow to infer where the photo was taken. Previously, this problem has been approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, this model achieves a 50 {\%} performance improvement over the single-image model.}, added-at = {2021-06-28T09:18:40.000+0200}, address = {Cham}, author = {Weyand, Tobias and Kostrikov, Ilya and Philbin, James}, biburl = {https://www.bibsonomy.org/bibtex/2b2d1845afea1fb721f8866121f6b8c7c/jaeschke}, booktitle = {Computer Vision -- ECCV 2016}, description = {PlaNet - Photo Geolocation with Convolutional Neural Networks | SpringerLink}, doi = {10.1007/978-3-319-46484-8_3}, editor = {Leibe, Bastian and Matas, Jiri and Sebe, Nicu and Welling, Max}, interhash = {3502527e0fe41170b6d118ce7ef38726}, intrahash = {b2d1845afea1fb721f8866121f6b8c7c}, isbn = {978-3-319-46484-8}, keywords = {classification cnn deep deeplearning geo geolocation image learning machine ml network neural planet}, pages = {37--55}, publisher = {Springer International Publishing}, timestamp = {2024-08-21T13:56:21.000+0200}, title = {PlaNet - Photo Geolocation with Convolutional Neural Networks}, url = {https://link.springer.com/chapter/10.1007/978-3-319-46484-8_3}, year = 2016 }