Road type classification of MLS point clouds using deep learning

Q. Bai*, R. C. Lindenbergh, J. Vijverberg, J. A.P. Guelen

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

3 Citations (Scopus)
63 Downloads (Pure)


Functional classification of the road is important to the construction of sustainable transport systems and proper design of facilities. Mobile laser scanning (MLS) point clouds provide accurate and dense 3D measurements of road scenes, while their massive data volume and lack of structure also bring difficulties in processing. 3D point cloud understanding through deep neural networks achieves breakthroughs since PointNet and arouses wide attention in recent years. In this paper, we study the automatic road type classification of MLS point clouds by employing a point-wise neural network, RandLA-Net, which is designed for consuming large-scale point clouds. An effective local feature aggregation (LFA) module in RandLA-Net preserves the local geometry in point clouds by formulating an enhanced geometric feature vector and learning different point weights in a local neighborhood. Based on this method, we also investigate possible feature combinations to calculate neighboring weights. We train on a colorized point cloud from the city of Hannover, Germany, and classify road points into 7 classes that reveal detailed functions, i.e., sidewalk, cycling path, rail track, parking area, motorway, green area, and island without traffic. Also, three feature combinations inside the LFA module are examined, including the geometric feature vector only, the geometric feature vector combined with additional features (e.g., color), and the geometric feature vector combined with local differences of additional features. We achieve the best overall accuracy (86.23%) and mean IoU (69.41%) by adopting the second and third combinations respectively, with additional features including Red, Green, Blue, and intensity. The evaluation results demonstrate the effectiveness of our method, but we also observe that different road types benefit the most from different feature settings.

Original languageEnglish
Pages (from-to)115-122
Number of pages8
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue numberB2-2021
Publication statusPublished - 2021
Event2021 24th ISPRS Congress Commission II: Imaging Today, Foreseeing Tomorrow - Virtual, Online, France
Duration: 5 Jul 20219 Jul 2021


  • Deep learning
  • Local feature aggregation
  • Mobile mapping
  • Point clouds
  • Road type
  • Semantic segmentation


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