Fusing Satellite Imagery and Planimetric Maps for Cross-View Localization
Quick Answer
This paper shows that A new fusion module enhances cross-view localization by integrating satellite imagery with planimetric maps, achieving a 30.13% reduction in mean localization error.
Quick Take
A new fusion module enhances cross-view localization by integrating satellite imagery with planimetric maps, achieving a 30.13% reduction in mean localization error. This method improves state-of-the-art single-modality approaches by utilizing cross-modal conditioning and patch-level fusion, allowing adaptive selection of the most informative modality.
Key Points
- Proposes a fusion module that combines satellite imagery and planimetric maps.
- Achieves a 30.13% reduction in mean localization error.
- Utilizes cross-modal conditioning for enhanced encoding awareness.
- Implements a patch-level fusion rule for controlled information exchange.
- Demonstrates improved accuracy over state-of-the-art single-modality methods.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 10166v1 Announce Type: new Abstract: Current cross-view localization methods predominantly rely on satellite imagery as the aerial modality. Although recent work explores planimetric maps (e. g. , OpenStreetMap tiles), these approaches often lag in performance. Yet both modalities are widely available and possess complementary properties. Satellite images are closer to ground-level camera imagery, offering finer detail, whereas planimetric maps contain annotated objects (e. g.
, streetlamps) and remain informative in areas where the ground is occluded, such as by foliage. Despite this, only one prior work provides an end-to-end method to fuse the two modalities, and it does not demonstrate their potential within state-of-the-art methods. To combine the strengths of both modalities, we propose a new fusion module that augments standard encoders and demonstrates that integrating satellite imagery with planimetric maps improves state-of-the-art single-modality methods.
The module comprises (i) cross-modal conditioning, which processes each modality's encoding with awareness of the other, and (ii) a patch-level fusion rule that controls the granularity of information exchange. We achieve state-of-the-art results, reducing the mean localization error by 30. 13\%. Qualitatively, the fusion adaptively selects the more informative modality, improving overall accuracy.
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