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  3. Without Paired Labeled Data: End-to-end Self-supervised Learning For Drone-view Geo-localization.
  1. Home
  3. Without Paired Labeled Data: End-to-end Self-supervised Learning For Drone-view Geo-localization.

Related Experiment Video

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Without Paired Labeled Data: End-to-End Self-Supervised Learning for Drone-View Geo-Localization.

Zhongwei Chen, Zhao-Xu Yang, Hai-Jun Rong

    IEEE Transactions on Neural Networks and Learning Systems
    |May 29, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a new self-supervised learning method for drone-view geo-localization, significantly improving accuracy without extensive labeled data. The dynamic memory-driven and neighborhood information learning method enhances drone localization capabilities in real-world scenarios.

    Related Experiment Videos

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    Area of Science:

    • Computer Vision
    • Geospatial Intelligence
    • Machine Learning

    Background:

    • Drone-view geo-localization (DVGL) traditionally requires extensive labeled drone-satellite image pairs for supervised learning.
    • Existing methods struggle with distribution shifts and high annotation costs, limiting practical open-world deployment.
    • Limited transferability of current DVGL techniques hinders adaptability to new geographical regions.

    Purpose of the Study:

    • To propose a novel end-to-end self-supervised learning method for DVGL that overcomes limitations of supervised approaches.
    • To enhance drone localization accuracy and robustness in diverse, open-world scenarios.
    • To reduce reliance on costly annotated data and improve model transferability.

    Main Methods:

    • Developed the dynamic memory-driven and neighborhood information learning (DMNIL) method, featuring a shallow backbone network.
  • Employed a clustering algorithm for pseudolabel generation and a dual-path contrastive learning framework for intra-view representation learning.
  • Integrated dynamic hierarchical memory learning (DHML) for feature consistency and information consistency evolution learning (ICEL) for cross-view alignment, enhanced by a pseudolabel enhancement (PLE) strategy.

    • Main Results:

    • The DMNIL method demonstrated superior performance compared to existing self-supervised DVGL techniques.
    • The proposed approach outperformed several state-of-the-art supervised methods on three benchmark datasets.
    • Achieved significant improvements in localization accuracy and robustness, validating the effectiveness of the self-supervised approach.

    Conclusions:

    • The DMNIL method offers a highly effective self-supervised solution for drone-view geo-localization, addressing key limitations of supervised learning.
    • The integration of dynamic memory and neighborhood information learning significantly enhances feature representation and alignment.
    • This research paves the way for more practical and scalable DVGL applications in real-world environments.