  Cross-view image matching for geo-localisation is a challenging problem due
to the significant visual difference between aerial and ground-level
viewpoints. The method provides localisation capabilities from geo-referenced
images, eliminating the need for external devices or costly equipment. This
enhances the capacity of agents to autonomously determine their position,
navigate, and operate effectively in environments where GPS signals are
unavailable. Current research employs a variety of techniques to reduce the
domain gap such as applying polar transforms to aerial images or synthesising
between perspectives. However, these approaches generally rely on having a
360{\deg} field of view, limiting real-world feasibility. We propose BEV-CV, an
approach which introduces two key novelties. Firstly we bring ground-level
images into a semantic Birds-Eye-View before matching embeddings, allowing for
direct comparison with aerial segmentation representations. Secondly, we
introduce the use of a Normalised Temperature-scaled Cross Entropy Loss to the
sub-field, achieving faster convergence than with the standard triplet loss.
BEV-CV achieves state-of-the-art recall accuracies, improving feature
extraction Top-1 rates by more than 300%, and Top-1% rates by approximately
150% for 70{\deg} crops, and for orientation-aware application we achieve a 35%
Top-1 accuracy increase with 70{\deg} crops.
