Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone

• URL: http://arxiv.org/abs/2512.02737v1
• Date: Tue, 02 Dec 2025 13:21:20 GMT
• Title: Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone
• Authors: Tristan Amadei, Enric Meinhardt-Llopis, Benedicte Bascle, Corentin Abgrall, Gabriele Facciolo,
• Abstract summary: We present a training paradigm that removes the need for UAV imagery during training by learning directly from satellite-view reference images.<n>This is achieved through a dedicated augmentation strategy that simulates the visual domain shift between satellite and real-world UAV views.<n>We introduce CAEVL, an efficient model designed to exploit this paradigm, and validate it on ViLD, a new and challenging dataset of real-world UAV images.
• Score: 11.74837809839014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image-based localization in GNSS-denied environments is critical for UAV autonomy. Existing state-of-the-art approaches rely on matching UAV images to geo-referenced satellite images; however, they typically require large-scale, paired UAV-satellite datasets for training. Such data are costly to acquire and often unavailable, limiting their applicability. To address this challenge, we adopt a training paradigm that removes the need for UAV imagery during training by learning directly from satellite-view reference images. This is achieved through a dedicated augmentation strategy that simulates the visual domain shift between satellite and real-world UAV views. We introduce CAEVL, an efficient model designed to exploit this paradigm, and validate it on ViLD, a new and challenging dataset of real-world UAV images that we release to the community. Our method achieves competitive performance compared to approaches trained with paired data, demonstrating its effectiveness and strong generalization capabilities.

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