Beyond 2D Matching: A Unified Single-Stage Framework for Geometry-Aware Cross-View Object Geo-Localization
arXiv:2606.30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.g., ground or drone) within a geo-tagged reference image (e.g., satellite). Existing approaches heavily rely on 2D appearance matching and are constrained by limited datasets lacking geometric metadata, diverse prompts, and standard field-of-view imagery. To address these intertwined challenges, we first introduce \dataset, a large-scale, high-fidelity building dataset comprising over 220,000 ground-satellite and drone-satellite pairs. It provides mul
The proliferation of drone technology and the increasing demand for precise geo-localization in various applications are driving innovations in cross-view object matching.
This development improves autonomous systems' ability to geo-localize objects from disparate visual data, crucial for defense, urban planning, and reconnaissance.
Existing 2D appearance matching limitations are being overcome by a unified framework that incorporates geometric metadata and diverse imagery, leading to more robust geo-localization.
- · Defense contractors
- · Autonomous navigation companies
- · Geospatial intelligence firms
- · Urban planning departments
- · Companies relying on outdated 2D matching techniques
- · Manual geo-localization services
More accurate and reliable geo-localization for drones and ground vehicles will become widely available.
Enhanced capabilities for surveillance, target identification, and infrastructure monitoring will emerge, bolstering national security.
The increased precision in mapping and object identification could lead to entirely new applications in smart city management and environmental monitoring.
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Read at arXiv cs.AI