Toolkit for preprocessing and feature calculation of point clouds created using airborne laser scanning

688 commits | Last update: May 20, 2022

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What laserchicken can do for you

  • Find neighboring points in your point cloud and describe them as feature values
  • Filter points spatially or by attribute
  • Normalize height for every point

Laserchicken is a user-extendable, cross-platform Python tool for extracting statistical properties (features in machine learning jargon) of flexibly defined subsets of point cloud data. Laserchicken loads a point-cloud from a LAS or LAZ or PLY file. After this, it can filter points by various criteria, and it can normalize the height. Laserchicken can load another point cloud, which contains targets. For every target point, Laserchicken computes its neighbors. Based on the list of neighbors, Laserchicken extracts features that effectively describe the neighborhood of each target point.

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  • Big data
  • Machine learning
Programming Language
  • Python
  • Apache-2.0
Source code

Participating organizations


  • Christiaan Meijer
    Netherlands eScience Center
  • Nicolas Renaud
    Netherlands eScience Center
  • Bouwe Andela
    Netherlands eScience Center
  • Yifat Dzigan
    Netherlands eScience Center
  • Faruk Diblen
    Netherlands eScience Center
  • Gijs van den Oord
    Netherlands eScience Center
  • Elena Ranguelova
    Netherlands eScience Center
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Contact person
Christiaan Meijer
Netherlands eScience Center

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