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NAME - Create a raster map from an assemblage of many coordinates using univariate statistics.


raster, import, LIDAR

SYNOPSIS help [-sgi] input=name output=name [method=string] [type=string] [fs=character] [x=integer] [y=integer] [z=integer] [zrange=min,max] [zscale=float] [percent=integer] [pth=integer] [trim=float] [--overwrite] [--verbose] [--quiet]


Scan data file for extent then exit
In scan mode, print using shell script style
Ignore broken lines
Allow output files to overwrite existing files
Verbose module output
Quiet module output


ASCII file containing input data (or "-" to read from stdin)
Name for output raster map
Statistic to use for raster values
Options: n,min,max,range,sum,mean,stddev,variance,coeff_var,median,percentile,skewness,trimmean
Default: mean
Storage type for resultant raster map
Default: FCELL
Field separator
Special characters: newline, space, comma, tab
Default: |
Column number of x coordinates in input file (first column is 1)
Default: 1
Column number of y coordinates in input file
Default: 2
Column number of data values in input file
Default: 3
Filter range for z data (min,max)
Scale to apply to z data
Default: 1.0
Percent of map to keep in memory
Options: 1-100
Default: 100
pth percentile of the values
Options: 1-100
Discard <trim> percent of the smallest and <trim> percent of the largest observations
Options: 0-50


The module will load and bin ungridded x,y,z ASCII data into a new raster map. The user may choose from a variety of statistical methods in creating the new raster. Gridded data provided as a stream of x,y,z points may also be imported.

Please note that the current region extents and resolution are used for the import. It is therefore recommended to first use the -s flag to get the extents of the input points to be imported, then adjust the current region accordingly, and only then proceed with the actual import. is designed for processing massive point cloud datasets, for example raw LIDAR or sidescan sonar swath data. It has been tested with datasets as large as tens of billion of points (705GB in a single file).

Available statistics for populating the raster are:

n number of points in cell
min minimum value of points in cell
max maximum value of points in cell
range range of points in cell
sum sum of points in cell
mean average value of points in cell
stddev standard deviation of points in cell
variance variance of points in cell
coeff_varcoefficient of variance of points in cell
median median value of points in cell
percentile  pth percentile of points in cell
skewness skewness of points in cell
trimmean trimmed mean of points in cell


Gridded data

If data is known to be on a regular grid can reconstruct the map perfectly as long as some care is taken to set up the region correctly and that the data's native map projection is used. A typical method would involve determining the grid resolution either by examining the data's associated documentation or by studying the text file. Next scan the data with's -s (or -g) flag to find the input data's bounds. GRASS uses the cell-center raster convention where data points fall within the center of a cell, as opposed to the grid-node convention. Therefore you will need to grow the region out by half a cell in all directions beyond what the scan found in the file. After the region bounds and resolution are set correctly with g.region, run using the n method and verify that n=1 at all places. r.univar can help. Once you are confident that the region exactly matches the data proceed to run using one of the mean, min, max, or median methods. With n=1 throughout, the result should be identical regardless of which of those methods are used.

Memory use

While the input file can be arbitrarily large, will use a large amount of system memory for large raster regions (10000x10000). If the module refuses to start complaining that there isn't enough memory, use the percent parameter to run the module in several passes. In addition using a less precise map format (CELL [integer] or FCELL [floating point]) will use less memory than a DCELL [double precision floating point] output map. Methods such as n, min, max, sum will also use less memory, while stddev, variance, and coeff_var will use more. The aggregate functions median, percentile, skewness and trimmed mean will use even more memory and may not be appropriate for use with arbitrarily large input files.

The default map type=FCELL is intended as compromise between preserving data precision and limiting system resource consumption. If reading data from a stdin stream, the program can only run using a single pass.

Setting region bounds and resolution

You can use the -s scan flag to find the extent of the input data (and thus point density) before performing the full import. Use g.region to adjust the region bounds to match. The -g shell style flag prints the extent suitable as parameters for g.region. A suitable resolution can be found by dividing the number of input points by the area covered. e.g.
wc -l inputfile.txt
g.region -p
# points_per_cell = n_points / (rows * cols)

g.region -e
# UTM location:
# points_per_sq_m = n_points / (ns_extent * ew_extent)

# Lat/Lon location:
# points_per_sq_m = n_points / (ns_extent * ew_extent*cos(lat) * (1852*60)^2)

If you only intend to interpolate the data with and, then there is little point to setting the region resolution so fine that you only catch one data point per cell -- you might as well use " -zbt" directly.


Points falling outside the current region will be skipped. This includes points falling exactly on the southern region bound. (to capture those adjust the region with "g.region s=s-0.000001"; see g.region)

Blank lines and comment lines starting with the hash symbol (#) will be skipped.

The zrange parameter may be used for filtering the input data by vertical extent. Example uses might include preparing multiple raster sections to be combined into a 3D raster array with, or for filtering outliers on relatively flat terrain.

In varied terrain the user may find that min maps make for a good noise filter as most LIDAR noise is from premature hits. The min map may also be useful to find the underlying topography in a forested or urban environment if the cells are over sampled.

The user can use a combination of output maps to create custom filters. e.g. use r.mapcalc to create a mean-(2*stddev) map. [In this example the user may want to include a lower bound filter in r.mapcalc to remove highly variable points (small n) or run r.neighbors to smooth the stddev map before further use.]


If the raster map is to be reprojected, it may be more appropriate to reproject the input points with m.proj or cs2cs before running

Interpolation into a DEM

The vector engine's topographic abilities introduce a finite memory overhead per vector point which will typically limit a vector map to approximately 3 million points (~ 1750^2 cells). If you want more, use the -b flag to skip building topology. Without topology, however, all you'll be able to do with the vector map is display with d.vect and interpolate with Run r.univar on your raster map to check the number of non-NULL cells and adjust bounds and/or resolution as needed before proceeding.

Typical commands to create a DEM using a regularized spline fit:

r.univar lidar_min -z feature=point in=lidar_min out=lidar_min_pt layer=0 in=lidar_min_pt elev=lidar_min.rst


Import the Jockey's Ridge, NC, LIDAR dataset (compressed file "lidaratm2.txt.gz"), and process it into a clean DEM:
# scan and set region bounds -s fs=, in=lidaratm2.txt out=test
g.region n=35.969493 s=35.949693 e=-75.620999 w=-75.639999
g.region res=0:00:00.075 -a

# create "n" map containing count of points per cell for checking density in=lidaratm2.txt out=lidar_n fs=, method=n zrange=-2,50

# check point density [rho = n_sum / (rows*cols)]
r.univar lidar_n | grep sum
# create "min" map (elevation filtered for premature hits) in=lidaratm2.txt out=lidar_min fs=, method=min zrange=-2,50

# set computational region to area of interest
g.region n=35:57:56.25N s=35:57:13.575N w=75:38:23.7W e=75:37:15.675W

# check number of non-null cells (try and keep under a few million)
r.univar lidar_min | grep '^n:'

# convert to points -z feature=point in=lidar_min out=lidar_min_pt

# interpolate using a regularized spline fit layer=0 in=lidar_min_pt elev=lidar_min.rst

# set color scale to something interesting
r.colors lidar_min.rst rule=bcyr -n -e

# prepare a 1:1:1 scaled version for NVIZ visualization (for lat/lon input)
r.mapcalc "lidar_min.rst_scaled = lidar_min.rst / (1852*60)"
r.colors lidar_min.rst_scaled rule=bcyr -n -e



If you encounter any problems (or solutions!) please contact the GRASS Development Team.


g.region, m.proj, r.fillnulls,, r.mapcalc, r.neighbors,,,, r.univar,,

v.lidar.correction, v.lidar.edgedetection, v.lidar.growing, v.outlier,

pv - The UNIX pipe viewer utility


Hamish Bowman
Department of Marine Science
University of Otago
New Zealand

Extended by Volker Wichmann to support the aggregate functions median, percentile, skewness and trimmed mean.

Last changed: $Date: 2014-09-02 00:52:18 -0700 (Tue, 02 Sep 2014) $

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