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Open-Source Data-Driven Cross-Domain Road Detection From Very High Resolution Remote Sensing Imagery.

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    This study introduces the OSM-DOER framework for improved road detection in remote sensing images. It enhances generalization by aligning spatial structures and learning domain-specific textures, outperforming existing methods.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Geographic Information Systems

    Background:

    • Deep learning models struggle with road detection in remote sensing images due to domain discrepancies.
    • Limited generalization ability of current methods hinders accurate road identification across different datasets.

    Purpose of the Study:

    • To propose an open-source data-driven domain-specific representation (OSM-DOER) framework for robust cross-domain road detection.
    • To address the challenge of distribution discrepancies between training and testing samples in road detection tasks.

    Main Methods:

    • Developed the domain-specific representation (DOER) framework to align spatial structure distributions and learn domain-specific texture information.
    • Utilized OpenStreetMap (OSM) road centerline data to generate target domain samples for supervised network training.
    • Implemented and validated the OSM-DOER framework on public (SpaceNet, DeepGlobe) and large-scale (UK, China) road datasets.

    Main Results:

    • The OSM-DOER framework demonstrated significant advantages over mainstream road detection methods.
    • Integrating OSM road centerline data proved highly effective for enhancing target domain representation.
    • Achieved high-precision road detection by overcoming generalization limitations.

    Conclusions:

    • The proposed OSM-DOER framework offers a superior solution for cross-domain road detection in very high resolution remote sensing images.
    • OpenStreetMap data is a valuable resource for improving the performance and generalizability of road detection models.
    • The framework shows great potential for practical applications requiring accurate road mapping from diverse remote sensing data.