Fusing Satellite Imagery and Planimetric Maps for Cross-View Localization

• URL: http://arxiv.org/abs/2606.10166v1
• Date: Mon, 08 Jun 2026 20:52:57 GMT
• Title: Fusing Satellite Imagery and Planimetric Maps for Cross-View Localization
• Abstract summary: Cross-view localization methods predominantly rely on satellite imagery as the aerial modality.<n>We propose a new fusion module that augments standard encoders.<n>We achieve state-of-the-art results, reducing the mean localization error by 30.13%.
• Score: 73.13365196885216
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Fusion-CAM: Integrating Gradient and Region-Based Class Activation Maps for Robust Visual Explanations [2.537406035246369]
  Class Activation Map (CAM) methods are widely adopted to visualize the input regions influencing model predictions.<n> Gradient-based approaches (e.g. Grad-CAM) provide highly discriminative, fine-grained details by computing of class activations.<n>Region-based approaches (e.g. Score-CAM) aggregate information over larger areas, capturing broader object coverage at the cost of over-smoothing.<n>We introduce Fusion-CAM, a novel framework that bridges this explanatory gap by unifying both paradigms through a dedicated fusion mechanism.
  arXiv  Detail & Related papers  (2026-03-05T17:16:29Z)
• EarthMapper: Visual Autoregressive Models for Controllable Bidirectional Satellite-Map Translation [50.433911327489554]
  We introduce EarthMapper, a novel framework for controllable satellite-map translation.<n>We also contribute CNSatMap, a large-scale dataset comprising 302,132 precisely aligned satellite-map pairs across 38 Chinese cities.<n> experiments on CNSatMap and the New York dataset demonstrate EarthMapper's superior performance.
  arXiv  Detail & Related papers  (2025-04-28T02:41:12Z)
• FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field [12.247977717070773]
  This article presents a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration. The proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps. The effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration.
  arXiv  Detail & Related papers  (2024-04-27T20:54:15Z)
• View Consistent Purification for Accurate Cross-View Localization [59.48131378244399]
  This paper proposes a fine-grained self-localization method for outdoor robotics. The proposed method addresses limitations in existing cross-view localization methods. It is the first sparse visual-only method that enhances perception in dynamic environments.
  arXiv  Detail & Related papers  (2023-08-16T02:51:52Z)
• Image-based Geolocalization by Ground-to-2.5D Map Matching [21.21416396311102]
  Methods often utilize cross-view localization techniques to match ground-view query images with 2D maps. We propose a new approach to learning representative embeddings from multi-modal data. By encoding crucial geometric cues, our method learns discriminative location embeddings for matching panoramic images and maps.
  arXiv  Detail & Related papers  (2023-08-11T08:00:30Z)
• Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image [91.29546868637911]
  This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method.
  arXiv  Detail & Related papers  (2022-04-10T19:16:58Z)
• Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction [110.61383502442598]
  We introduce a novel neural network framework termed Cross-Modal Message Propagation Network (CMMPNet) CMMPNet is composed of two deep Auto-Encoders for modality-specific representation learning and a tailor-designed Dual Enhancement Module for cross-modal representation refinement. Experiments on three real-world benchmarks demonstrate the effectiveness of our CMMPNet for robust road extraction.
  arXiv  Detail & Related papers  (2021-11-30T04:30:10Z)
• Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance [148.9832328803202]
  We model the infor-mation fusion within focal stack via graph networks. We build a novel dual graph modelto guide the focal stack fusion process using all-focus pat-terns.
  arXiv  Detail & Related papers  (2021-10-02T00:54:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.