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Voxel segmentation-based 3D building detection algorithm for airborne LIDAR data.

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This study introduces a novel voxel segmentation algorithm for 3D building detection using Light Detection and Ranging (LIDAR) data. The method effectively separates buildings from non-building structures with high accuracy.

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Area of Science:

  • Geospatial Science
  • Computer Vision
  • Remote Sensing

Background:

  • Traditional Light Detection and Ranging (LIDAR) data representations have limitations.
  • Voxel-based representations offer explicit 3D structure but are underutilized in building detection algorithms.

Purpose of the Study:

  • To develop and evaluate a voxel segmentation-based algorithm for 3D building detection using LIDAR data.
  • To segment LIDAR point clouds into building and non-building voxels.
  • To improve the accuracy and efficiency of automated building detection.

Main Methods:

  • LIDAR point clouds are voxelized into a grayscale structure based on point intensity.
  • 3D-connected regions are segmented using voxel connectivity and grayscale similarity.
  • Building roofs and facades are detected sequentially based on geometric and location characteristics.

Main Results:

  • The algorithm successfully separates building and non-building voxels.
  • High detection rates were achieved on ISPRS benchmark data.
  • Average completeness, correctness, and quality indexes were 90.0%, 96.0%, and 88.1% respectively.

Conclusions:

  • Voxel segmentation is a promising approach for 3D building detection with LIDAR data.
  • The developed algorithm demonstrates high performance and accuracy.
  • This method offers a robust solution for automated building extraction from LIDAR datasets.