Enhancing Cross-View Geo-Localization through Global-Local Quadrant Interaction Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhancing Cross-View Geo-Localization through Global-Local Quadrant Interaction Network Jin Xu, Junping Yin, Juan Zhang, Tianyan Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6263145/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Cross-view geo-localization aims to match images of the same location captured from different perspectives, such as drone and satellite views. This task is inherently challenging due to significant visual discrepancies caused by viewpoint variations. Existing approaches often rely on global descriptors or limited directional cues, failing to effectively integrate diverse spatial information and global-local interactions.To address these limitations, we propose the Global-Local Quadrant Interaction Network (GLQINet), which enhances feature representation through two key components: the Quadrant Insight Module (QIM) and the Integrated Global-Local Attention Module (IGLAM). QIM partitions feature maps into directional quadrants, refining multi-scale spatial representations while preserving intra-class consistency. Meanwhile, IGLAM bridges global and local features by aggregating high-association feature stripes, reinforcing semantic coherence and spatial correlations.Extensive experiments on the University-1652 and SUES-200 benchmarks demonstrate that GLQINet significantly improves geo-localization accuracy, achieving state-of-the-art performance and effectively mitigating cross-view discrepancies. Physical sciences/Mathematics and computing Physical sciences/Engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviews received at journal 07 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers agreed at journal 28 Jun, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Editor assigned by journal 28 Mar, 2025 Editor invited by journal 28 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 19 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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