Title: Indoor 3.6M : A Dataset and Benchmark for Indoor Image Geolocation
Primary area: datasets and benchmarks
Keywords: geolocation, dataset, geolocalisaton

Abstract: Image geolocation has advanced rapidly for outdoor imagery, driven by large-scale benchmarks and strong visual cues such as landmarks, skylines, and vegetation. In contrast, indoor image geolocation remains underexplored: indoor scenes lack distinctive geographic features, are highly ambiguous, and are not adequately represented in existing datasets. We address this gap by introducing the first large-scale benchmark for indoor geolocation, consisting of \textbf{3.6 million} images across \textbf{213 countries}. We finetune state-of-the-art CLIP-based models such as Pigeon and GeoCLIP and report performance at country and continent levels using both top-$k$ accuracy as well as distance based accuracy metrics. Results highlight that continent-level geolocation is feasible, but fine grained indoor geolocation e.g street and city level geolocation remains an open challenge. This work defines a new frontier for geolocation research and provides the resources to advance it.

TL;DR: We present Indoor-Geo, a large-scale dataset and benchmark for indoor image geolocation.