From Street to Orbit: Training-Free Cross-View Retrieval via Location Semantics and LLM Guidance

• URL: http://arxiv.org/abs/2511.09820v1
• Date: Fri, 14 Nov 2025 01:11:19 GMT
• Title: From Street to Orbit: Training-Free Cross-View Retrieval via Location Semantics and LLM Guidance
• Authors: Jeongho Min, Dongyoung Kim, Jaehyup Lee,
• Abstract summary: Cross-view image retrieval is critical for applications such as autonomous navigation, urban planning, and localization in GPS-denied environments.<n>We present a simple yet effective cross-view image retrieval framework that leverages a pretrained vision encoder and a large language model (LLM)<n>Despite using no ground-truth supervision or finetuning, our proposed method outperforms prior learning-based approaches on the benchmark dataset under zero-shot settings.
• Score: 10.533095161205358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 using no 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/.

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