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    Area of Science:

    • Geospatial Science
    • Remote Sensing Technology
    • Computer Vision

    Background:

    • Airborne lidar data filtering is crucial for urban applications.
    • Existing methods face challenges with complex urban environments and parameter tuning.
    • Accurate filtering is essential for reliable 3D city modeling and analysis.

    Purpose of the Study:

    • To develop an automatic morphological filter for airborne lidar data.
    • To address limitations of complex parameter settings and low accuracy in urban lidar filtering.
    • To enhance the efficiency and reliability of lidar data processing in complex urban settings.

    Main Methods:

    • An automatic morphological filter utilizing a series of morphological top-hat operations.
    • Adaptive threshold calculation based on gradient changes for optimal filtering.
    • Evaluation using seven publicly available lidar datasets from the International Society for Photogrammetry and Remote Sensing.

    Main Results:

    • The proposed automatic filter achieved an average total error of 4.07%.
    • An average kappa coefficient of 90.90% was recorded, indicating high classification accuracy.
    • The method demonstrated superior performance compared to other existing lidar filtering techniques.

    Conclusions:

    • The automatic morphological filter effectively addresses challenges in urban lidar data processing.
    • Adaptive parameter determination leads to improved filtering accuracy and reliability.
    • This approach offers a robust solution for airborne lidar post-processing in complex urban environments.