Local and global encoder network for semantic segmentation of Airborne laser scanning point clouds

Yaping Lin, George Vosselman, Yanpeng Cao, Michael Ying Yang

Research output: Contribution to journalArticlepeer-review

32 Citations (SciVal)


Interpretation of Airborne Laser Scanning (ALS) point clouds is a critical procedure for producing various geo-information products like 3D city models, digital terrain models and land use maps. In this paper, we present a local and global encoder network (LGENet) for semantic segmentation of ALS point clouds. Adapting the KPConv network, we first extract features by both 2D and 3D point convolutions to allow the network to learn more representative local geometry. Then global encoders are used in the network to exploit contextual information at the object and point level. We design a segment-based Edge Conditioned Convolution to encode the global context between segments. We apply a spatial-channel attention module at the end of the network, which not only captures the global interdependencies between points but also models interactions between channels. We evaluate our method on two ALS datasets namely, the ISPRS benchmark dataset and DCF2019 dataset. For the ISPRS benchmark dataset, our model achieves state-of-the-art results with an overall accuracy of 0.845 and an average F1 score of 0.737. With regards to the DFC2019 dataset, our proposed network achieves an overall accuracy of 0.984 and an average F1 score of 0.834.

Original languageEnglish
Pages (from-to)151-168
Number of pages18
JournalISPRS Journal of Photogrammetry and Remote Sensing
Early online date30 Apr 2021
Publication statusPublished - 30 Jun 2021


  • Attention models
  • Global context
  • Point clouds
  • Semantic segmentation

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences


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