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arXiv:2603.08491v3 Announce Type: replace Abstract: Cross-modal Geo-localization (CMGL) matches ground-level text descriptions with geo-tagged aerial imagery, which is crucial for pedestrian navigati...
UniGeo is a multimodal large language model designed for text-guided drone geo‑localization, enabling the identification of target regions in large image galleries from natural‑language descriptions. It integrates geo‑semantic understanding, cross‑view semantic generation, and candidate‑level verification within a shared vision‑language framework, establishing stable correspondences among local scene elements, spatial relations, and language. A multi‑stage training strategy progressively refines geo‑semantic learning, cross‑view mapping, and fine‑grained verification, yielding significant performance gains on GeoText‑1652, with R@10 and mAP improvements of 13.59 and 2.83 percentage points respectively.
arXiv:2609.02012v2 Announce Type: replace Abstract: Point-of-interest (POI) localization matches user-provided storefront close-ups to the same shops in wide, geo-tagged vehicle-mounted street views....
arXiv:2608. 16658v1 Announce Type: cross Abstract: Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images.
arXiv:2610.09761v1 Announce Type: cross Abstract: Text-to-point-cloud localization estimates a position in a city-scale 3D map from descriptions of surrounding objects. Existing coarse-to-fine method...
arXiv:2606.08918v2 Announce Type: replace Abstract: Worldwide image geo-localization aims to determine where on Earth a single image was captured. However, visually similar scenes may lie thousands o...