| title | Trajectory-aware Cross-view Geo-localization with Sequential Observations |
|---|---|
| authors | Tianyi Gao, Jiayu Lin, Danielle Beaulieu, Nathan Jacobs |
| date | 2026-07-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.15491 |
| affiliation | Multimodal Vision Research Laboratory @ WashU |
| upvotes | 7 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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 sim39K 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 https://humblegamer.github.io/trajloc/. |
cite
@misc{gao2026trajectory,
title={Trajectory-aware Cross-view Geo-localization with Sequential Observations},
author={Tianyi Gao and Jiayu Lin and Danielle Beaulieu and Nathan Jacobs},
year={2026},
eprint={2607.15491},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.15491}
}