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

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[Submitted on 16 Jul 2026]

Title:Trajectory-aware Cross-view Geo-localization with Sequential Observations

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Abstract:Cross-view geo-localization matches ground-level observations against geo-tagged satellite imagery. Recent methods show that sequential queries such as video clips yield richer spatiotemporal cues than single images, yet they overlook a complementary sequential modality: route descriptions -- which capture the same trajectory at a higher level of abstraction and are often the only input available (e.g., a user directing an autonomous vehicle to a pickup point). To bridge this gap, we introduce SeqGeo-VL, a dataset of $\sim$39K video-text-satellite triplets, and TrajLoc, a unified framework capable of processing both video clips and route descriptions. By leveraging both dense visual and abstract linguistic semantics, TrajLoc enables these modalities to mutually reinforce cross-view matching. We further propose TrajMod, a lightweight module that conditions query embeddings on trajectory geometry, yielding spatially-aware representations. Experiments show that TrajLoc achieves substantial gains over state-of-the-art methods on both video and text geo-localization. The project page is available at this https URL.

Comments: Accepted to ECCV 2026. Project Page: this https URL

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.15491 [cs.CV]

(or arXiv:2607.15491v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.15491

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Tianyi Gao [view email] [v1] Thu, 16 Jul 2026 22:39:19 UTC (5,906 KB)

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