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This paper introduces UniGeo, a unified multimodal large language model designed for text-guided drone geo-localization, addressing the limitations of existing methods that rely on direct matching between text queries and candidate images. By integrating geo-semantic understanding, cross-view semantic generation, and candidate-level verification, UniGeo establishes stable correspondences among local scene elements and language descriptions, enhancing fine-grained localization. Experimental results on the GeoText-1652 dataset show significant improvements in retrieval performance, with R@10 and mAP increasing by 13.59 and 2.83 percentage points, respectively.
UniGeo achieves a remarkable 13.59-point improvement in retrieval accuracy for text-guided drone geo-localization by leveraging a unified multimodal framework.
Text-guided drone geo-localization aims to identify a target region in a large-scale image gallery from a natural-language description. Existing methods mainly formulate this task as direct matching between an open-ended text query and candidate images. However, incomplete queries and highly similar candidates often make global cross-modal matching insufficient for reliable fine-grained localization. We propose UniGeo, a unified multimodal large language model (MLLM) for text-guided drone geo-localization. Built on a shared vision-language framework, UniGeo jointly supports geo-semantic understanding, cross-view semantic generation, and candidate-level verification. Specifically, it establishes stable correspondences among local scene elements, spatial relations, and language descriptions through geo-semantic learning, and further models semantic mappings between drone and satellite views through cross-view generation. Based on these capabilities, a plug-and-play verification module performs fine-grained discrimination among highly confusable candidates. We further introduce a multi-stage training strategy that progressively learns geo-semantic understanding, cross-view generation, and candidate verification, improving adaptation to text-guided geo-localization. Experiments demonstrate consistent improvements across multiple retrieval backbones. On GeoText-1652, UniGeo improves R@10 and mAP by 13.59 and 2.83 percentage points, respectively, validating its effectiveness for fine-grained text-guided drone geo-localization.
