Trajectory-aware Cross-view Geo-Localization with Sequential Observations

Cross-view geo-localization matches ground-level observationsagainst geo-tagged satellite imagery. Recent methods show that sequen-tial queries such as video clips yield richer spatiotemporal cues thansingle images, yet they overlook a complementary sequential modality:route descriptions—which capture the same trajectory at a higher levelof abstraction and are often the only input available (e.g., a user direct-ing an autonomous vehicle to a pickup point). To bridge this gap, weintroduce SeqGeo-VL, a dataset of ∼39K video–text–satellite triplets,and TrajLoc, a unified framework capable of processing both video clipsand route descriptions. By leveraging both dense visual and abstract lin-guistic semantics, TrajLoc enables these modalities to mutually reinforcecross-view matching. We further propose TrajMod, a lightweight mod-ule that conditions query embeddings on trajectory geometry, yieldingspatially-aware representations. Experiments show that TrajLoc achievessubstantial gains over state-of-the-art methods on both video and textgeo-localization. Code, model weights, and the dataset are released athttps://humblegamer.github.io/trajloc/.