Trajectory-aware Cross-view Geo-localization with Sequential Observations
Tianyi Gao, Jiayu Lin, Danielle Beaulieu, Nathan Jacobs
This paper develops a new method for cross-view geo-localization that uses video clips and route descriptions to improve accuracy, and introduces a unified framework that can handle both modalities. Practitioners in autonomous vehicle development or geospatial analysis might care about this research as it aims to address a common challenge in these fields.
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/.