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    Area of Science:

    • Geospatial Science
    • Computer Vision
    • Remote Sensing

    Background:

    • Very-high-resolution (VHR) remote sensing imagery is crucial for precise vector mapping of geographical features.
    • Deep learning methods for large images often use sliding windows, causing performance degradation due to stitching challenges.
    • Existing methods struggle with the computational demands and stitching artifacts when processing large VHR imagery.

    Purpose of the Study:

    • To develop a novel global context-aware local point optimization method for accurate vector mapping of large-size VHR remote sensing images.
    • To address the limitations of GPU memory and stitching difficulties in current deep learning-based vector mapping approaches.
    • To enable full-scope vector mapping by effectively utilizing consistent cues within large-scale remote sensing data.

    Main Methods:

    • A pyramid fusion network (PFNet) was proposed for end-to-end semantic segmentation of large-size images, leveraging global context.
    • An inflection-point perception network (IPNet) was developed to generate stable boundary points under semantic segmentation constraints.
    • The method integrates global semantic information with local point generation for accurate boundary depiction.

    Main Results:

    • The proposed method achieved superior performance in vector mapping tasks on large-scale building, water, and road datasets.
    • Experiments demonstrated significant improvements over existing methods, particularly on images exceeding 100 million pixels.
    • The global context-aware approach effectively mitigates performance degradation associated with sliding block strategies.

    Conclusions:

    • The novel global context-aware local point optimization method offers a robust solution for high-precision vector mapping of large VHR remote sensing imagery.
    • PFNet and IPNet effectively address the challenges of limited GPU memory and stitching difficulties, enabling accurate mapping.
    • The developed approach significantly advances the state-of-the-art in automated geographical element extraction from large-scale remote sensing data.