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.

By understanding the hidden locational clues in images, entirely new approaches of analyzing the natural and built environment are being opened up with profound implications for a number of fields, ranging from the recognition of weather, season, and climate patterns to rural and urban scene understanding, and improvements in navigation and self-driving car technology. Since the beginning of 2022, image geolocalization has additionally garnered extensive media coverage for becoming an immediate priority of investigative journalists and open source intelligence (OSINT) researchers in their attempt to verify information and to document war atrocities in Ukraine, extracting geolocational information from social media content. Despite high academic and public interest, image geolocalization remains an extremely challenging problem. This is because training datasets are geographically sparse, often limited to specific countries, and biased towards urban or rural scenes. The task is further complicated by the fact that geolocalization requires reasoning on multiple levels of geographic granularity (e.g.