# Fusing Satellite Imagery and Planimetric Maps for Cross-View Localization

arXiv: https://arxiv.org/abs/2606.10166 · HTML: https://mlsanity.com/paper/2606.10166/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 24 of 24)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval](https://mlsanity.com/paper/2011.12172/) (2020)
- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator](https://mlsanity.com/paper/2308.16906/) (2023)
- [Uncertainty-aware Vision-based Metric Cross-view Geolocalization](https://mlsanity.com/paper/2211.12145/) (2022)
- [Visual Cross-View Metric Localization with Dense Uncertainty Estimates](https://mlsanity.com/paper/2208.08519/) (2022)
- [SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation](https://mlsanity.com/paper/2211.14651/) (2022)
- [Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer](https://mlsanity.com/paper/2307.08015/) (2023)
- [Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching](https://mlsanity.com/paper/2203.14148/) (2022)
- [SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding](https://mlsanity.com/paper/2306.05407/) (2023)
- [Convolutional Cross-View Pose Estimation](https://mlsanity.com/paper/2303.05915/) (2023)
- [LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments](https://mlsanity.com/paper/2104.09169/) (2021)
- [UniBEV: Multi-modal 3D Object Detection with Uniform BEV Encoders for Robustness against Missing Sensor Modalities](https://mlsanity.com/paper/2309.14516/) (2023)
- [FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching](https://mlsanity.com/paper/2503.18725/) (2025)
- [LASER: LAtent SpacE Rendering for 2D Visual Localization](https://mlsanity.com/paper/2204.00157/) (2022)
- [Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization](https://mlsanity.com/paper/2309.15556/) (2023)
- [F3Loc: Fusion and Filtering for Floorplan Localization](https://mlsanity.com/paper/2403.03370/) (2024)
- [MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps](https://mlsanity.com/paper/2407.08561/) (2024)
- [View Consistent Purification for Accurate Cross-View Localization](https://mlsanity.com/paper/2308.08110/) (2023)
- [C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation](https://mlsanity.com/paper/2312.08060/) (2023)
- [Loc2: Interpretable Cross-View Localization via Depth-Lifted Local Feature Matching](https://mlsanity.com/paper/2509.09792/) (2025)
- [OSMLoc: Single Image-Based Visual Localization in OpenStreetMap with Fused Geometric and Semantic Guidance](https://mlsanity.com/paper/2411.08665/) (2024)
- [PIDLoc: Cross-View Pose Optimization Network Inspired by PID Controllers](https://mlsanity.com/paper/2503.02388/) (2025)

## Abstract (arXiv)

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.