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An essential type of geospatial data for the identification of complex objects is light detection and ranging (LiDAR) data of 3D point clouds obtained from laser sensors. LiDAR data offers geometric information in terms of 3D coordinates together with other properties such as intensity and multiple returns. The novel combination of the Opals program system and Sparse Convolutional Neural Network (CNN) models is the main focus of this project, which processes and categorizes Lidar data collected from Vienna, Austria. The prediction results have excellent performance on Test tiles situated at Vienna city such as ground, vegetation and building.

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Using Opals Program System and Sparse CNN Model in Processing and Classifying Airborne Laser Scanning Data

  • 会议论文

  • 发表日期：

    2025年

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  • Nguyen Vu Hai,
  • Duc-Binh Nguyen,
  • Tran Quang Quy,
  • Kim-Son Nguyen,
  • Vu Duc Thai

摘要

An essential type of geospatial data for the identification of complex objects is light detection and ranging (LiDAR) data of 3D point clouds obtained from laser sensors. LiDAR data offers geometric information in terms of 3D coordinates together with other properties such as intensity and multiple returns. The novel combination of the Opals program system and Sparse Convolutional Neural Network (CNN) models is the main focus of this project, which processes and categorizes Lidar data collected from Vienna, Austria. The prediction results have excellent performance on Test tiles situated at Vienna city such as ground, vegetation and building.