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Conference Paper
From Street to Orbit: Training-Free Cross-View Retrieval via Location Semantics and LLM Guidance
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- Authors
- Jeongho Min, Dongyoung Kim, Jaehyup Lee
- Issue Date
- 2026-03
- Citation
- Winter Conference on Applications of Computer Vision (WACV) 2026, pp.570-579
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Cross-view image retrieval, particularly street-to-satellite matching, is a critical task for applications such as autonomous navigation, urban planning, and localization in GPS-denied environments. However, existing approaches often require supervised training on curated datasets and rely on panoramic or UAV-based images, which limits real-world deployment. In this paper, we present a simple yet effective cross-view image retrieval framework that leverages a pretrained vision encoder and a large language model (LLM), requiring no additional training. Given a monocular street-view image, our method extracts geographic cues through web-based image search and LLM-based location inference, generates a satellite query via geocoding API, and retrieves matching tiles using a pretrained vision encoder (e.g., DINOv2) with PCA-based whitening feature refinement. Despite not using ground-truth supervision or finetuning, our proposed method outperforms prior learning-based approaches on the benchmark dataset under zero-shot settings. Moreover, our pipeline enables automatic construction of semantically aligned street-to-satellite datasets, which is offering a scalable and cost-efficient alternative to manual annotation. All source codes will be made publicly available at https://jeonghomin.github.io/ street2orbit.github.io/.
- KSP Keywords
- Automatic construction, Benchmark datasets, Cost-efficient, Critical task, Cross-view retrieval, Existing Approaches, GPS-denied environments, Image retrieval, Language Models, Learning-based, Location semantics