PIGEON: Predicting Image Geolocations

TL;DR

This paper introduces PIGEON and PIGEOTTO, two advanced image geolocalization models that significantly improve accuracy and generalization across unseen locations using novel training techniques and retrieval methods.

Contribution

The paper presents a new geolocalization system combining semantic geocell creation, multi-task contrastive pretraining, and location cluster retrieval, achieving state-of-the-art results and better generalization.

Findings

PIGEON places over 40% of guesses within 25 km globally.

PIGEOTTO outperforms previous SOTA by up to 7.7% on city accuracy.

PIGEOTTO generalizes effectively to unseen locations.

Abstract

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy, success in prior literature is constrained to narrow distributions of images of landmarks, and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation, multi-task contrastive pretraining, and a novel loss function. Additionally, our work is the first to perform retrieval over location clusters for guess refinements. 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. We also develop a bot and deploy PIGEON in a blind experiment against humans, ranking in the top 0.01% of players. We further challenge one of the world's foremost professional Geoguessr players to a series of six matches with millions of viewers, winning all six games. Our second model, PIGEOTTO, differs in that it is trained on a dataset of images from Flickr and Wikipedia, achieving state-of-the-art results on a wide range of image geolocalization benchmarks, outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate, planet-scale image geolocalization systems. Our code is available on GitHub.