  This paper investigates the effective utilization of unlabeled data for
large-area cross-view geo-localization (CVGL), encompassing both unsupervised
and semi-supervised settings. Common approaches to CVGL rely on
ground-satellite image pairs and employ label-driven supervised training.
However, the cost of collecting precise cross-view image pairs hinders the
deployment of CVGL in real-life scenarios. Without the pairs, CVGL will be more
challenging to handle the significant imaging and spatial gaps between ground
and satellite images. To this end, we propose an unsupervised framework
including a cross-view projection to guide the model for retrieving initial
pseudo-labels and a fast re-ranking mechanism to refine the pseudo-labels by
leveraging the fact that ``the perfectly paired ground-satellite image is
located in a unique and identical scene". The framework exhibits competitive
performance compared with supervised works on three open-source benchmarks. Our
code and models will be released on https://github.com/liguopeng0923/UCVGL.
