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R Package for Fast Airborne LiDAR Data Processing

The lasR package (pronounce laser) does not intent to supersede the lidR package, but is designed to be much more efficient than lidR for common tasks like the production of CHM, DTM, tree detection and segmentation on large coverages. lidR intends to be a tool box to make data exploration and innovation easy. lasR on another hand focuses on production, being optimized for memory and speed and makes no trade off with other aspects of the development.

📖 Read the tutorial to start with lasR


There is currently no plan for releasing lasR on CRAN. lasR is hosted on r-universe:

install.packages('lasR', repos = '')

Users can’t rely on the CRAN versioning system and RStudio update button to get the latest version of lasR. When loading lasR with library(lasR), an internal routine checks for the latest version and prints a message if a new version is available. Updates are more frequent this way.

#> lasR 0.1.3 is now available. You are using 0.1.1
#> install.packages('lasR', repos = '')


The following benchmark compares how much time and RAM memory it takes for lasR and lidR to produce a DTM, a CHM, and a raster with two metrics derived from Z and intensity. The test was performed on 120 million points stored in 4 LAZ files. More details in the benchmark vignette.

Main differences with lidR

  • Introduces the concept of pipelines, that is missing in lidR, to chain multiple operations on a point cloud optimally.
  • Is written exclusively in C/C++ without a single line of R. The R code is only an API to a standalone C++ software.
  • Does not load the point cloud into a data.frame. The point cloud is stored in a C++ structure that is not exposed to users.
  • Has only 1 strong dependencies to gdal. But if sf and terra are installed the experience is better.

More details in the corresponding vignette


lasR is developed at Laval University.

  • For lasR:
    • © 2023-2024 Jean-Romain Roussel
    • Provided under GPL-3 license.
  • For LASlib and LASzip:
    • © 2007-2021 Martin Isenburg -
    • Provided under LGPL license and modified to be R-compliant by Jean-Romain Roussel.
  • For chm_prep:
  • For json parser:
  • For delaunator:
  • For Armadillo:
    • © 2008-2024 Conrad Sanderson (
    • © 2008-2016 National ICT Australia (NICTA)
    • © 2017-2024 Data61 / CSIRO
    • Provided under Apache license
  • For Cloth Simulation Filter (CSF)
    • © 2017 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University
    • Provided under Apache License
    • W. Zhang, J. Qi, P. Wan, H. Wang, D. Xie, X. Wang, and G. Yan, “An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation,” Remote Sens., vol. 8, no. 6, p. 501, 2016.