Photo geolocation estimation
9 papers with code • 6 benchmarks • 3 datasets
Photo geolocation estimation is task of estimate or classify the geolocation from photos on world map.
Most implemented papers
PlaNet - Photo Geolocation with Convolutional Neural Networks
Is it possible to build a system to determine the location where a photo was taken using just its pixels?
Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification
While the successful estimation of a photo's geolocation enables a number of interesting applications, it is also a very challenging task.
Interpretable Semantic Photo Geolocation
State-of-the-art methods treat the task as a classification problem, where the choice of the classes, that is the partitioning of the world map, is crucial for the performance.
GeoWINE: Geolocation based Wiki, Image,News and Event Retrieval
The first module is a state-of-the-art model for geolocation estimation of images.
Where in the World is this Image? Transformer-based Geo-localization in the Wild
Predicting the geographic location (geo-localization) from a single ground-level RGB image taken anywhere in the world is a very challenging problem.
Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization
Image geolocalization is the challenging task of predicting the geographic coordinates of origin for a given photo.
PIGEON: Predicting Image Geolocations
We train two models for evaluations on street-level data and general-purpose image geolocalization; the first model, PIGEON, is trained on data from the game of Geoguessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally.
GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization
Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth.
OpenStreetView-5M: The Many Roads to Global Visual Geolocation
Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms.