  Cross-view geo-localization aims to estimate the GPS location of a query
ground-view image by matching it to images from a reference database of
geo-tagged aerial images. To address this challenging problem, recent
approaches use panoramic ground-view images to increase the range of
visibility. Although appealing, panoramic images are not readily available
compared to the videos of limited Field-Of-View (FOV) images. In this paper, we
present the first cross-view geo-localization method that works on a sequence
of limited FOV images. Our model is trained end-to-end to capture the temporal
structure that lies within the frames using the attention-based temporal
feature aggregation module. To robustly tackle different sequences length and
GPS noises during inference, we propose to use a sequential dropout scheme to
simulate variant length sequences. To evaluate the proposed approach in
realistic settings, we present a new large-scale dataset containing ground-view
sequences along with the corresponding aerial-view images. Extensive
experiments and comparisons demonstrate the superiority of the proposed
approach compared to several competitive baselines.
