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

• URL: http://arxiv.org/abs/2412.17007v1
• Date: Sun, 22 Dec 2024 13:13:10 GMT
• Title: Where am I? Cross-View Geo-localization with Natural Language Descriptions
• Authors: Junyan Ye, Honglin Lin, Leyan Ou, Dairong Chen, Zihao Wang, Conghui He, Weijia Li,
• Abstract summary: Cross-view geo-localization identifies the locations of street-view images by matching them with geo-tagged satellite images or OSM. 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.
• Score: 16.870286138129902
• License:
• Abstract: Cross-view geo-localization identifies the locations of street-view images by matching them with geo-tagged satellite images or OSM. However, most 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. 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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