An efficient deep learning approach for ground point filtering in aerial laser scanning point clouds

A. Nurunnabi*, F. N. Teferle, J. Li, R. C. Lindenbergh, A. Hunegnaw

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

10 Citations (Scopus)
215 Downloads (Pure)


Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds' irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling.
Original languageEnglish
Pages (from-to)31-38
Number of pages8
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue numberB1-2021
Publication statusPublished - 2021
Event2021 24th ISPRS Congress Commission I: Imaging Today, Foreseeing Tomorrow - Nice, France
Duration: 5 Jul 20219 Jul 2021


  • Classification
  • CNN
  • Feature Extraction
  • LiDAR
  • Local Feature
  • Neural Network
  • PointNet
  • Semantic Analysis


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