R Package for Fast Airborne LiDAR Data Processing
The lasR package (pronounced "laser") is an R package designed to provide a platform to share efficient implementation of tools designed with the lidR package. It enables the creation and execution of complex processing pipelines on massive lidar data. It can read and write .las, .laz and .pcd files, compute metrics using an area-based approach, generate digital canopy models, segment individual trees, thin point data, and process collections of files using multicore processing. lasR offers a range of tools to process massive volumes of lidar data efficiently in a production environment after the R&D phase with lidR.
lasR.lasR in R with: install.packages('lasR', repos = 'https://r-lidar.r-universe.dev').lasR. It is free and open source, but requires time and effort to develop and maintain.lasR is not intended to replace the lidR package. While lidR is tailored for academic research, lasR focuses on production scenarios, offering significantly higher efficiency compared to lidR. For more details, see the comparison.
There are no current plans to release lasR on CRAN. Instead, it is hosted on r-universe:
install.packages('lasR', repos = 'https://r-lidar.r-universe.dev')
Since lasR is not available on CRAN, users cannot rely on the CRAN versioning system or the RStudio update button to get the latest version. Instead, when lasR is loaded with library(lasR), an internal routine checks for the latest version and notifies the user if an update is available. This approach allows for more frequent updates, ensuring users have access to the newest features and bug fixes without waiting for a formal release cycle.
library(lasR)
#> lasR 0.1.3 is now available. You are using 0.1.1
#> install.packages('lasR', repos = 'https://r-lidar.r-universe.dev')
Here is a simple example of how to classify outliers before to produce a Digital Surface Model (DSM) and a Digital Terrain Model (DTM) from a folder containing airborne LiDAR point clouds. For more examples see the tutorial.
library(lasR) folder = "/folder/of/laz/tiles/" pipeline = classify_with_sor() + delete_noise() + chm(1) + dtm(1) exec(pipeline, on = folder, ncores = 16, progress = T)Main Differences with
lidR
The following benchmark compares the time and RAM usage of lasR and lidR for producing a Digital Terrain Model (DTM), a Canopy Height Model (CHM), and a raster containing two metrics derived from elevation (Z) and intensity. The test was conducted on 120 million points stored in 4 LAZ files. For more details, check out the benchmark vignette.
lasR introduces pipelines to optimally chain multiple operations on a point cloud, a feature not available in lidR.lasR uses more powerful algorithms designed for speed and efficiency.lasR has no R code except for the API interface. This makes it highly optimized for performance.lidR, which loads the point cloud into an R data.frame, lasR stores point clouds in a C++ structure that is not exposed to the user, minimizing memory usage.lasR has a single strong dependency on gdal. If sf and terra are installed, the user experience is enhanced, but they are not mandatory.For more details, see the relevant vignette.
lasR is free and open source and relies on other free and open source tools.
lasR:
LASlib and LASzip:
chm_prep:
json parser:
delaunator:
Eigen:
Cloth Simulation Filter (CSF)
lasR is developed openly by r-lidar.
The initial development of lasR was made possible through the financial support of Laval University. To continue the development of this free software, we now offer consulting, programming, and training services. For more information, please visit our website.
sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install libgdal-dev libgeos-dev libproj-dev
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