Wrivinder: Towards Spatial Intelligence for Geo-locating Ground Images onto Satellite Imagery

• URL: http://arxiv.org/abs/2602.14929v1
• Date: Mon, 16 Feb 2026 17:06:54 GMT
• Title: Wrivinder: Towards Spatial Intelligence for Geo-locating Ground Images onto Satellite Imagery
• Authors: Chandrakanth Gudavalli, Tajuddin Manhar Mohammed, Abhay Yadav, Ananth Vishnu Bhaskar, Hardik Prajapati, Cheng Peng, Rama Chellappa, Shivkumar Chandrasekaran, B. S. Manjunath,
• Abstract summary: 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.
• Score: 28.971555127858334
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Aligning ground-level imagery with geo-registered satellite maps is crucial for mapping, navigation, and situational awareness, yet remains challenging under large viewpoint gaps or when GPS is unreliable. We introduce Wrivinder, a zero-shot, geometry-driven framework that aggregates multiple ground photographs to reconstruct a consistent 3D scene and align it with overhead satellite imagery. Wrivinder combines SfM reconstruction, 3D Gaussian Splatting, semantic grounding, and monocular depth--based metric cues to produce a stable zenith-view rendering that can be directly matched to satellite context for metrically accurate camera geo-localization. To support systematic evaluation of this task, which lacks suitable benchmarks, we also release MC-Sat, a curated dataset linking multi-view ground imagery with geo-registered satellite tiles across diverse outdoor environments. Together, Wrivinder and MC-Sat provide a first comprehensive baseline and testbed for studying geometry-centered cross-view alignment without paired supervision. In zero-shot experiments, Wrivinder achieves sub-30\,m geolocation accuracy across both dense and large-area scenes, highlighting the promise of geometry-based aggregation for robust ground-to-satellite localization.

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