Pixel-wise Geo-registration of Drone and Satellite Images

• URL: http://arxiv.org/abs/2608.28891v1
• Date: Fri, 28 Aug 2026 21:50:29 GMT
• Title: Pixel-wise Geo-registration of Drone and Satellite Images
• Abstract summary: Pixel-level cross-view geo-registration aims to align a query image (e.g., drone) to a geo-referenced satellite map so that every query pixel can be mapped to real-world GPS coordinates.<n>Existing benchmarks largely provide only GPS labels, limiting evaluation to a single coordinate per image.<n>We introduce SkyReg, a dataset and standardized benchmark for pixel-level drone-to-satellite geo-registration.
• Score: 46.14662444002411
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pixel-level cross-view geo-registration aims to align a query image (e.g., drone) to a geo-referenced satellite map so that every query pixel can be mapped to real-world GPS coordinates. Despite strong progress in cross-view geo-localization, existing benchmarks largely provide only GPS labels, limiting evaluation to a single coordinate per image and leaving dense geodetic alignment underexplored. We introduce SkyReg, a dataset and standardized benchmark for pixel-level drone-to-satellite geo-registration, providing dense per-pixel geo-location supervision across diverse settings (orthographic and perspective), scene types (urban, landmark-centric, suburban/rural), and camera configurations. Using SkyReg, we evaluate a broad set of baselines spanning retrieval, feature matching, homography-based alignment, and feed-forward 3D reconstruction. Finally, cross-view pairs from SkyReg, we train a geometry-aware reconstruction pipeline that achieves state-of-the-art results,improving performance by a significant margin.

Related papers

• Spotter: Efficient Urban Visual Localization via Geo-Referenced Facade Landmarks in GPS-Degraded Environments [0.5371337604556311]
  Spotter is a real-time visual localization framework for robotic and wearable platforms.<n>It processes Google Street View panoramas by semantically segmenting facades and pairing multi-view stereo depth with cartographic data.<n>At runtime, query images are matched via a cascaded retrieval and geometric verification pipeline to recover fine-grained global camera localization.
  arXiv  Detail & Related papers  (2026-08-24T14:15:25Z)
• VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale [46.71012068113612]
  The task of video geolocalization aims to determine the precise GPS coordinates of a video's origin and map its trajectory.<n>We propose VidTAG, a dual-encoder framework that performs frame-to-GPS retrieval using both self-supervised and language-aligned features.<n>Our approach enables fine-grained video geolocalization and lays a strong foundation for future research.
  arXiv  Detail & Related papers  (2026-04-14T00:35:07Z)
• Wrivinder: Towards Spatial Intelligence for Geo-locating Ground Images onto Satellite Imagery [28.971555127858334]
  We introduce Wrivinder, a framework that aggregates multiple ground photographs to align it with overhead satellite imagery.<n>We also release MC-Sat, a curated dataset linking multi-view ground imagery with geo-registered satellite tiles across diverse outdoor environments.<n>In zero-shot experiments, Wrivinder achieves sub-30,m geolocation accuracy across both dense and large-area scenes.
  arXiv  Detail & Related papers  (2026-02-16T17:06:54Z)
• GeoBridge: A Semantic-Anchored Multi-View Foundation Model Bridging Images and Text for Geo-Localization [36.20562550716618]
  Cross-view geo-localization infers a location by retrieving geo-tagged reference images that visually correspond to a query image.<n>Traditional satellite-centric paradigm limits robustness when high-resolution or up-to-date satellite imagery is unavailable.<n>We propose GeoBridge, a model that performs bidirectional matching across views and supports language-to-image retrieval.
  arXiv  Detail & Related papers  (2025-12-02T12:28:22Z)
• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components.<n>GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.<n>We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• Game4Loc: A UAV Geo-Localization Benchmark from Game Data [0.0]
  We introduce a more practical UAV geo-localization task including partial matches of cross-view paired data.<n>Experiments demonstrate the effectiveness of our data and training method for UAV geo-localization.
  arXiv  Detail & Related papers  (2024-09-25T13:33:28Z)
• Weakly-supervised Camera Localization by Ground-to-satellite Image Registration [52.54992898069471]
  We propose a weakly supervised learning strategy for ground-to-satellite image registration. It derives positive and negative satellite images for each ground image. We also propose a self-supervision strategy for cross-view image relative rotation estimation.
  arXiv  Detail & Related papers  (2024-09-10T12:57:16Z)
• GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
  Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
  arXiv  Detail & Related papers  (2023-09-27T20:54:56Z)
• Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching [102.39635336450262]
  We address the problem of ground-to-satellite image geo-localization by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image.
  arXiv  Detail & Related papers  (2022-03-26T20:10:38Z)

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.