r.in.pdal

Creates a raster map from LAS LiDAR points using univariate statistics and r.in.xyz.

r.in.pdal [-sg] input=name output=name [,name,...] [resolution=float] [raster_reference=string] [raster_file=string] [method=string [,string,...]] [zrange=min,max] [zscale=float] [type=string] [percent=integer] [pth=integer] [trim=float] [footprint=string] [pdal_cmd=string] [--overwrite] [--verbose] [--quiet] [--qq] [--ui]

Example:

r.in.pdal input=name output=name

grass.script.run_command("r.in.pdal", input, output, resolution=1.0, raster_reference=None, raster_file=None, method="mean", zrange=None, zscale=1.0, type="FCELL", percent=100, pth=None, trim=None, footprint=None, pdal_cmd="pdal", flags=None, overwrite=False, verbose=False, quiet=False, superquiet=False)

Example:

gs.run_command("r.in.pdal", input="name", output="name")

Parameters

Command linePython (grass.script)

input=name [required]
    LAS input file
output=name [,name,...] [required]
    Name for output raster map
resolution=float
    2D grid resolution (north-south and east-west)
    Default: 1.0
raster_reference=string
    Raster map to be used as pixel geometry reference
    Raster map to align to, e.g. an orthophoto of the same region
raster_file=string
    External raster map to be used as pixel geometry reference
    External raster map to align to, e.g. an orthophoto of the same region
method=string [,string,...]
    Statistic to use for raster values
    Allowed values: n, min, max, range, sum, mean, stddev, variance, coeff_var, median, percentile, skewness, trimmean
    Default: mean
    n: Number of points in cell
    min: Minimum value of point values in cell
    max: Maximum value of point values in cell
    range: Range of point values in cell
    sum: Sum of point values in cell
    mean: Mean (average) value of point values in cell
    stddev: Standard deviation of point values in cell
    variance: Variance of point values in cell
    coeff_var: Coefficient of variance of point values in cell
    median: Median value of point values in cell
    percentile: Pth (nth) percentile of point values in cell
    skewness: Skewness of point values in cell
zrange=min,max
    Filter range for z data (min,max)
zscale=float
    Scale to apply to z data
    Default: 1.0
type=string
    Type of raster map to be created / Storage type for resultant raster map
    Allowed values: CELL, FCELL, DCELL
    Default: FCELL
    CELL: Integer
    FCELL: Single precision floating point
    DCELL: Double precision floating point
percent=integer
    Percent of map to keep in memory
    Allowed values: 1-100
    Default: 100
pth=integer
    Pth percentile of the values
    Allowed values: 1-100
trim=float
    Discard <trim> percent of the smallest and <trim> percent of the largest observations
    Allowed values: 1-50
footprint=string
    Footprint of the data as vector map
pdal_cmd=string
    Command for PDAL (e.g. if PDAL runs only in a docker)
    Default: pdal
-s
    Scan data file for extent then exit
-g
    In scan mode, print using shell script style
--overwrite
    Allow output files to overwrite existing files
--help
    Print usage summary
--verbose
    Verbose module output
--quiet
    Quiet module output
--qq
    Very quiet module output
--ui
    Force launching GUI dialog

input : str, required
    LAS input file
    Used as: input, raster, name
output : str | list[str], required
    Name for output raster map
    Used as: output, raster, name
resolution : float, optional
    2D grid resolution (north-south and east-west)
    Default: 1.0
raster_reference : str, optional
    Raster map to be used as pixel geometry reference
    Raster map to align to, e.g. an orthophoto of the same region
raster_file : str, optional
    External raster map to be used as pixel geometry reference
    External raster map to align to, e.g. an orthophoto of the same region
method : str | list[str], optional
    Statistic to use for raster values
    Allowed values: n, min, max, range, sum, mean, stddev, variance, coeff_var, median, percentile, skewness, trimmean
    n: Number of points in cell
    min: Minimum value of point values in cell
    max: Maximum value of point values in cell
    range: Range of point values in cell
    sum: Sum of point values in cell
    mean: Mean (average) value of point values in cell
    stddev: Standard deviation of point values in cell
    variance: Variance of point values in cell
    coeff_var: Coefficient of variance of point values in cell
    median: Median value of point values in cell
    percentile: Pth (nth) percentile of point values in cell
    skewness: Skewness of point values in cell
    Default: mean
zrange : tuple[float, float] | list[float] | str, optional
    Filter range for z data (min,max)
    Used as: min,max
zscale : float, optional
    Scale to apply to z data
    Default: 1.0
type : str, optional
    Type of raster map to be created / Storage type for resultant raster map
    Allowed values: CELL, FCELL, DCELL
    CELL: Integer
    FCELL: Single precision floating point
    DCELL: Double precision floating point
    Default: FCELL
percent : int, optional
    Percent of map to keep in memory
    Allowed values: 1-100
    Default: 100
pth : int, optional
    Pth percentile of the values
    Allowed values: 1-100
trim : float, optional
    Discard <trim> percent of the smallest and <trim> percent of the largest observations
    Allowed values: 1-50
footprint : str, optional
    Footprint of the data as vector map
pdal_cmd : str, optional
    Command for PDAL (e.g. if PDAL runs only in a docker)
    Default: pdal
flags : str, optional
    Allowed values: s, g
    s
        Scan data file for extent then exit
    g
        In scan mode, print using shell script style
overwrite: bool, optional
    Allow output files to overwrite existing files
    Default: False
verbose: bool, optional
    Verbose module output
    Default: False
quiet: bool, optional
    Quiet module output
    Default: False
superquiet: bool, optional
    Very quiet module output
    Default: False

DESCRIPTION

r.in.pdal creates a raster map from LAS LiDAR points using the Point Data Abstraction Library (PDAL) by binning the points into a raster map using univariate statistics. The user may choose from a variety of statistical methods which will be used for binning when creating the new raster map.

The computational region of a LiDAR point file can be determined by scanning the file using the -s flag.

Optionally r.in.pdal creates an estimated vector footprint area map of the LAS file when using the footprint parameter (the footprint is generated by PDAL).

Since a new raster map is created during the binning, the binning of points depends on the computational region settings (extent and resolution) which is by default set to the extent of the LiDAR input file (see more about binning below). The resulting raster resolution can be specified with the parameter resolution.

r.in.pdal is designed for processing massive point cloud datasets, for example raw LiDAR or sidescan sonar swath data. It has been tested with large datasets.

For details concerning raster binning see the manual page of r.in.lidar.

NOTES

r.in.pdal uses g.region to set the extent and resolution of the resulting raster and internally r.in.xyz to import the LiDAR points.

EXAMPLES

Import LAS file using PDAL

Import of a LAS file using PDAL into a mapset in the sample NC SPM location. The sample LAS file is available here.

# check metadata
pdal info --summary lidar_raleigh_nc_spm_height_feet.las

# scan extent and exit
r.in.pdal input=lidar_raleigh_nc_spm_height_feet.las output=lidar_raleigh -s

# scan extent (g.region style) and exit
r.in.pdal input=lidar_raleigh_nc_spm_height_feet.las output=lidar_raleigh -s -g
# n=228500 s=215000.01 w=633370.82 e=645000 t=558.87 b=88.5

# set computation region to this extent
g.region n=228500 s=215000.01 w=633370.82 e=645000 -p

# import while aligning pixel geometry to existing "elevation" 10m res. raster map
# specifying EPSG manually because SRS information is missing in this LAS file
r.in.pdal input=lidar_raleigh_nc_spm_height_feet.las raster_reference=elevation \
          resolution=10 output=lidar_raleigh method=mean

# optionally: footprint=lidar_raleigh_footprint

# visualize
d.mon wx0
g.list vector
d.rast lidar_raleigh
d.vect streets_wake

# analyse differences between DEM and rasterized point cloud
# LAS files come with height in US feet units
r.mapcalc "diff = elevation - lidar_raleigh * 0.3048006096012192"
r.univar -e diff

Figure: View showing the difference of the elevation map and LiDAR point data binned to raster map with r.in.pdal

Figure: LiDAR cloud footprint

Import LAZ file using PDAL docker

Import of a LAZ file using PDAL docker into a mapset in the sample NC SPM location. The sample LAZ file is available here.

# pulling official PDAL docker image
docker pull pdal/pdal

# using PDAL docker as command with mounted data volume
# (caution: the LAZ file has to be stored in the mounted folder! (here: $(pwd)))
export pdal_docker="docker run --rm -v $(pwd):/data -t pdal/pdal pdal"

# scan extent and exit
r.in.pdal input=/data/simple.laz output=lidar -s pdal_cmd="$pdal_docker"

# scan extent (g.region style) and exit
r.in.pdal input=/data/simple.laz output=lidar -s -g pdal_cmd="$pdal_docker"
# n=853535.43 s=848899.7 w=635619.85 e=638982.55 t=586.38 b=406.59

# set computation region to this extent
g.region n=853535.43 s=848899.7 w=635619.85 e=638982.55 -p

# import data
r.in.pdal input=/data/simple.laz output=lidar_perc95 method=percentile pth=95 pdal_cmd="$pdal_docker"

r.univar lidar_perc95

AUTHORS

Anika Bettge, mundialis GmbH & Co. KG
Vaclav Petras, NCSU GeoForAll Lab (projection, computational region)

Documentation: Markus Neteler, mundialis GmbH & Co. KG

SOURCE CODE

Available at: r.in.pdal source code (history)
Latest change: Thursday Mar 20 21:35:59 2025 in commit f270357