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Worldwide Geo-localization aims to pinpoint the precise location of imagestaken anywhere on Earth. This task has considerable challenges due to immensevariation in geographic landscapes. The image-to-image retrieval-basedapproaches fail to solve this problem on a global scale as it is not feasibleto construct a large gallery of images covering the entire world. Instead,existing approaches divide the globe into discrete geographic cells,transforming the problem into a classification task. However, their performanceis limited by the predefined classes and often results in inaccuratelocalizations when an image's location significantly deviates from its classcenter. To overcome these limitations, we propose GeoCLIP, a novelCLIP-inspired Image-to-GPS retrieval approach that enforces alignment betweenthe image and its corresponding GPS locations. GeoCLIP's location encodermodels the Earth as a continuous function by employing positional encodingthrough random Fourier features and constructing a hierarchical representationthat captures information at varying resolutions to yield a semantically richhigh-dimensional feature suitable to use even beyond geo-localization. To thebest of our knowledge, this is the first work employing GPS encoding forgeo-localization. We demonstrate the efficacy of our method via extensiveexperiments and ablations on benchmark datasets. We achieve competitiveperformance with just 20% of training data, highlighting its effectiveness evenin limited-data settings. Furthermore, we qualitatively demonstrategeo-localization using a text query by leveraging CLIP backbone of our imageencoder. The project webpage is available at:https://vicentevivan.github.io/GeoCLIP

GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization