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arXiv · 2412.17007

Where am I? Cross-View Geo-localization with Natural Language Descriptions

Junyan Ye·Honglin Lin·Leyan Ou·Dairong Chen·Zihao Wang·Qi Zhu·Conghui He·Weijia Li
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Abstract

Cross-view geo-localization identifies the locations of street-view images by matching them with geo-tagged satellite images or OSM. However, most existing studies focus on image-to-image retrieval, with fewer addressing text-guided retrieval, a task vital for applications like pedestrian navigation and emergency response. In this work, we introduce a novel task for cross-view geo-localization with natural language descriptions, which aims to retrieve corresponding satellite images or OSM database based on scene text descriptions. To support this task, we construct the CVG-Text dataset by collecting cross-view data from multiple cities and employing a scene text generation approach that leverages the annotation capabilities of Large Multimodal Models to produce high-quality scene text descriptions with localization details. Additionally, we propose a novel text-based retrieval localization method, CrossText2Loc, which improves recall by 10% and demonstrates excellent long-text retrieval capabilities. In terms of explainability, it not only provides similarity scores but also offers retrieval reasons. More information can be found at https://yejy53.github.io/CVG-Text/ .

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Junyan Ye, Honglin Lin, Leyan Ou, Dairong Chen, Zihao Wang, Qi Zhu, Conghui He, Weijia Li. 2024-12-22. Where am I? Cross-View Geo-localization with Natural Language Descriptions. https://arxiv.org/abs/2412.17007

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