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WAGL: Extreme Weather Adaptive Method for Robust and Generalizable UAV-based Cross-View Geo-localization

Published: 28 October 2024 Publication History

Abstract

As drones become increasingly utilized across various fields, related multimedia applications are also emerging. One significant application is cross-view geo-localization, which leverages aerial drone and satellite imagery data to facilitate drone navigation and geo-localization. In this paper, we focus on the robustness and generalization of retrieval under various extreme weather conditions. Considering the significant gap between training and testing data, our research emphasizes exploring and employing a powerful self-supervised backbone and an unsupervised aggregator to achieve domain adaptation. Additionally, from a data perspective, we simulate various weather conditions to bridge the gap between training and testing drone data through data augmentation. Futhermore, a cross-weather triplet loss is utilized to minimize the domain differences between drone and satellite images under extreme weather conditions. Our method achieves 94.07% Recall@1 accuracy on University-160k-WX, and ranks 4th in the UAVM2024 Challenge. Code will be released at https://github.com/SunJ1025/WAGL.

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  1. WAGL: Extreme Weather Adaptive Method for Robust and Generalizable UAV-based Cross-View Geo-localization

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    cover image ACM Conferences
    UAVM '24: Proceedings of the 2nd Workshop on UAVs in Multimedia: Capturing the World from a New Perspective
    October 2024
    41 pages
    ISBN:9798400712067
    DOI:10.1145/3689095
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 28 October 2024

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    Author Tags

    1. cross-view
    2. extreme weather
    3. geo-localization
    4. image retrieval
    5. self-supervised

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    • Short-paper

    Funding Sources

    • This work was supported by -- Shenzhen Science and Technology Innovation Committee (File No. SGDX20220530111001006), and the Hong Kong and Macau Joint Research and Development Fund of Wuyi University (File No. 2021WGALH19).

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    MM '24
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    MM '24: The 32nd ACM International Conference on Multimedia
    October 28 - November 1, 2024
    Melbourne VIC, Australia

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