Trajectory-aware Cross-view Geo-localization with Sequential Observations
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| Authors | Tianyi Gao et al. |
| Year | 2026 |
| HF Upvotes | 7 |
| arXiv | 2607.15491 |
| Download | |
| HF Page | View on Hugging Face |
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/.
Engineering Breakdown
The Problem
Cross-view geo-localization matches ground-level observations against geo-tagged satellite imagery.
The Approach
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.
Key Results
Experiments show that TrajLoc achieves substantial gains over state-of-the-art methods on both video and text geo-localization.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Trajectoryaware
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