lidR (version 2.0.0)

lasfilterdecimate: Decimate a LAS object


Reduce the number of points using several possible algorithms.


lasfilterdecimate(las, algorithm)



An object of class LAS or LAScatalog.


function. An algorithm of point decimation. lidR have: random, homogenize and highest.


If the input is a LAS object, returns a LAS object. If the input is a LAScatalog, returns a LAScatalog.

Working with a <code>LAScatalog</code>

This section appears in each function that supports a LAScatalog as input.

In lidR when the input of a function is a LAScatalog the function uses the LAScatalog processing engine. The user can modify the engine options using the available options. A careful reading of the engine documentation is recommended before processing LAScatalogs. Each lidR function should come with a section that documents the supported engine options.

The LAScatalog engine supports .lax files that significantly improve the computation speed of spatial queries using a spatial index. Users should really take advantage a .lax files, but this is not mandatory.

Supported processing options

Supported processing options for a LAScatalog (in bold). For more details see the LAScatalog engine documentation:

  • chunk size: How much data is loaded at once.

  • chunk buffer: This function guarantee a strict wall-to-wall continuous output. The buffer option is not considered.

  • chunk alignment: Align the processed chunks.

  • cores: How many cores are used. More cores means more data is loaded at once.

  • progress: Displays a progression estimation.

  • output_files*: Mandatory because the output is likely to be too big to be returned in R and needs to be written in las/laz files. Supported templates are XLEFT, XRIGHT, YBOTTOM, YTOP, XCENTER, YCENTER ID and, if chunk is size equal to 0 (processing by file), ORIGINALFILENAME.

  • laz_compression: write las or laz files

  • select: The function will write files equivalent to the original ones. Thus select = "*" and cannot be changed.

  • filter: Read only points of interest.


Run this code
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las = readLAS(LASfile, select = "xyz")

# Select points randomly to reach an overall density of 1
thinned1 = lasfilterdecimate(las, random(1))

# Select points randomly to reach an homogeneous density of 1
thinned2 = lasfilterdecimate(las, homogenize(1,5))

# Select the highest point within each pixel of an overlayed grid
thinned3 = lasfilterdecimate(las, highest(5))
# }

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