# GeoBridge++: Fact-Guided Geo-Semantic Bridging for Unified Cross-View Geo-Localization

arXiv: https://arxiv.org/abs/2512.02697 · HTML: https://mlsanity.com/paper/2512.02697/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2025.

## Built on by (most cited first, 5 of 5)

- [Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework](https://mlsanity.com/paper/2603.08491/) (2026)
- [SkyLink: A Large Vision-Language Model Driven Re-ranking Framework for Cross-View UAV geolocalization](https://mlsanity.com/paper/2603.08063/) (2026)
- [MAPS: Multi-Anchor Projection Similarity for Joint Vision-Language Geo-Localization](https://mlsanity.com/paper/2606.22543/) (2026)
- [GeoMFD: Continual Drone-View Geo-Localization with Geometry-Aware Adapter and Margin-Field Distillation](https://mlsanity.com/paper/2607.25788/) (2026)
- [Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization](https://mlsanity.com/paper/2609.18139/) (2026)

## Abstract (arXiv)

Cross-view geo-localization infers a location by retrieving geo-tagged reference images matching a query image. However, the traditional satellite-centric paradigm limits robustness when high-resolution or up-to-date satellite imagery is unavailable and underexploits complementary cues across views and modalities. To address these challenges, we propose GeoBridge, a novel model that performs bidirectional matching across views and supports language-to-image retrieval. GeoBridge builds on a novel semantic-anchor mechanism that bridges multi-view features through textual descriptions for robust, flexible localization. We further extend GeoBridge to propose GeoBridge++, a fact-guided geo-semantic bridging framework incorporating real-world geographic knowledge to reduce the ambiguity and instability in appearance-dominated supervision. It integrates structured geographic attributes with visual observations to construct factual descriptions and applies targeted guidance based on modality-specific observable content, thereby enhancing geographic discriminability. GeoBridge++ exploits explicit spatial structures encoded by static maps to build a geo-semantic bridge that adaptively aggregates complementary multi-view information and promotes cross-view consistency. In support of this task, we further construct GeoLoc-MM, a million-scale, multi-view, and multi-scale dataset with aligned drone, satellite, street-view, and static-map imagery at six spatial extents per location, enabling systematic evaluation of arbitrary cross-view retrieval, scale robustness, and cross-view generalization. Extensive experiments show that GeoBridge supports robust cross-view and cross-modal geo-localization, while GeoBridge++ achieves consistent improvements across multiple benchmarks. Code and dataset will be released at https://github.com/MiliLab/GeoBridge.