r.in.xyz(1grass)

okay so bestie, welcome to the iconic r.in.xyz(1grass) page from the GRASS User's Manual. You want to turn a whole pile of loose points into an actual raster map? You came to the right girl. Sit down, grab a boba, we're doing this 💖

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💖 NAME 💖

r.in.xyz — creates a raster map from an assemblage of many coordinates using univariate statistics. That's the whole vibe. Many points in, one beautiful raster out ✨

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🔑 KEYWORDS 🔑

raster, import, statistics, conversion, aggregation, binning, ASCII, LIDAR

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✨ SYNOPSIS ✨

r.in.xyz
r.in.xyz --help
r.in.xyz [-sgi] input=name output=name  [method=string]
[separator=character]   [x=integer]   [y=integer]   [z=integer]
[skip=integer]   [zrange=min,max]   [zscale=float]
[value_column=integer]   [vrange=min,max]   [vscale=float]
[type=string]   [percent=integer]   [pth=integer]   [trim=float]
[--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

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🚩 Flags 🚩

• -s — scan the data file for its extent, then dip out (exit). Like checking the map before you actually drive there 🗺️
• -g — in scan mode, print using shell script style. Very eval-core, very demure 🐚
• -i — ignore broken lines. If a line is messy, we simply do not perceive it 🙈
• --overwrite — allow output files to overwrite existing files. No cap, it will absolutely replace them 💀
• --help — print usage summary
• --verbose — verbose module output
• --quiet — quiet module output 🤫
• --ui — force launching GUI dialog

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🎛️ Parameters 🎛️

• input=name [required] — ASCII file containing input data (or "-" to read from stdin). Yes bestie, you can pipe it in 🫶
• output=name [required] — name for output raster map
• method=string — statistic to use for raster values
  • Options: 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
  • trimmean: Trimmed mean of point values in cell
• separator=character — field separator
  • Special characters: pipe, comma, space, tab, newline
  • Default: pipe
• x=integer — column number of x coordinates in input file (first column is 1)
  • Default: 1
• y=integer — column number of y coordinates in input file
  • Default: 2
• z=integer — column number of data values in input file
  • If a separate value column is given, this option refers to the z-coordinate column to be filtered by the zrange option
  • Default: 3
• skip=integer — number of header lines to skip at top of input file
  • Default: 0
• zrange=min,max — filter range for z data (min,max)
• zscale=float — scale to apply to z data
  • Default: 1.0
• value_column=integer — alternate column number of data values in input file
  • If not given (or set to 0) the z-column data is used
  • Default: 0
• vrange=min,max — filter range for alternate value column data (min,max)
• vscale=float — scale to apply to alternate value column data
  • Default: 1.0
• type=string — type of raster map to be created
  • Storage type for resultant raster map
  • Options: 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
  • Options: 1-100
  • Default: 100
• pth=integer — Pth percentile of the values
  • Options: 1-100
• trim=float — discard <trim> percent of the smallest and <trim> percent of the largest observations
  • Options: 0-50

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📖 DESCRIPTION 📖

The r.in.xyz module will load and bin ungridded x,y,z ASCII data into a shiny 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. Points go in, raster comes out, nobody gets hurt 🌸

⚠️ 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. Do not skip the vibe check first, bestie 🫠

r.in.xyz 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). She's been through things. She's built different 💅

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Available statistics for populating the raster are (method):

• 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_var — coefficient 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
  • Variance and derivatives use the biased estimator (n). [subject to change]
  • Coefficient of variance is given in percentage and defined as (stddev/mean)*100.

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It is also possible to bin and store another data column (e.g. backscatter) while simultaneously filtering and scaling both the data column values and the z range. Multi-tasking queen behavior honestly 👑

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<divider>

💅 NOTES 💅

📐 Gridded data

okay so bestie, listen — if your data is already on a regular grid, r.in.xyz can reconstruct the map perfectly. like, flawlessly. but there's a catch: you gotta set up the region correctly and make sure you're using the data's native map projection. no cap.

A typical method would involve:

1. figuring out the grid resolution — either by examining the data's associated documentation or by studying the text file
2. scanning the data with r.in.xyz's -s (or -g) flag to find the input data's bounds

Here's the thing though — GRASS uses the cell-center raster convention, where data points fall within the center of a cell, as opposed to the grid-node convention. So you'll 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 r.in.xyz using the n method and verify that n=1 at all places. r.univar can help with that. Once you're confident that the region exactly matches the data, proceed to run r.in.xyz 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 you use. slay. ✨

🧠 Memory use

While the input file can be arbitrarily large, r.in.xyz 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.

Also, 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 a 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 project:
# points_per_sq_m = n_points / (ns_extent * ew_extent)
# Lat/Lon project:
# points_per_sq_m = n_points / (ns_extent * ew_extent*cos(lat) * (1852*60)^2)

If you only intend to interpolate the data with r.to.vect and v.surf.rst, 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 "v.in.ascii -zbt" directly. 😌

🧹 Filtering

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 r.to.rast3, or for filtering outliers on relatively flat terrain.

In varied terrain, you might find that min maps make for a good noise filter, since 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.

You can also use a combination of r.in.xyz output maps to create custom filters. e.g. use r.mapcalc to create a mean-(2*stddev) map. [In this example you 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.]

🎚️ Alternate value column

The value_column parameter can be used in specialized cases when you want to filter by z-range but bin and store another column's data. For example, if you wanted to look at backscatter values between 1000 and 1500 meters elevation. 🛰️

This is particularly useful when using r.in.xyz to prepare depth slices for a 3D raster — the zrange option defines the depth slice but the data values stored in the voxels describe an additional dimension. As with the z column, a filtering range and scaling factor may be applied.

🔄 Reprojection

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 r.in.xyz. just saying. 💁

🏔️ 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 (~ 1750² cells). 😳

If you want more, use the r.to.vect -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 v.surf.rst.

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
r.to.vect -z type=point in=lidar_min out=lidar_min_pt
v.surf.rst in=lidar_min_pt elev=lidar_min.rst

🔢 Import of x,y,string data

r.in.xyz is expecting numeric values as z column. In order to perform an occurrence count operation even on x,y data with non-numeric attribute(s), the data can be imported using either the x or y coordinate as a fake z column for method=n (count number of points per grid cell) — the z values are ignored anyway. iconic. 👑

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🌈 EXAMPLES 🌈

🏗️ Import of x,y,z ASCII into DEM

Sometimes elevation data are delivered as x,y,z ASCII files instead of a raster matrix. The import procedure consists of a few steps: calculation of the map extent, setting of the computational region accordingly with an additional extension into all directions by half a raster cell in order to register the elevation points at raster cell centers.

Note: if the z column is separated by several spaces from the coordinate columns, it may be sufficient to adapt the z position value.

# Important: observe the raster spacing from the ASCII file:
# ASCII file format (example):
# 630007.5 228492.5 141.99614
# 630022.5 228492.5 141.37904
# 630037.5 228492.5 142.29822
# 630052.5 228492.5 143.97987
# ...
# In this example the distance is 15m in x and y direction.
# detect extent, print result as g.region parameters
r.in.xyz input=elevation.xyz separator=space -s -g
# ... n=228492.5 s=215007.5 e=644992.5 w=630007.5 b=55.578793 t=156.32986
# set computational region, along with the actual raster resolution
# as defined by the point spacing in the ASCII file:
g.region n=228492.5 s=215007.5 e=644992.5 w=630007.5 res=15 -p
# now enlarge computational region by half a raster cell (here 7.5m) to
# store the points as cell centers:
g.region n=n+7.5 s=s-7.5 w=w-7.5 e=e+7.5 -p
# import XYZ ASCII file, with z values as raster cell values
r.in.xyz input=elevation.xyz separator=space method=mean output=myelev
# univariate statistics for verification of raster values
r.univar myelev

🛸 Import of LiDAR data and DEM creation

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
r.in.xyz -s -g separator="," in=lidaratm2.txt
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
r.in.xyz in=lidaratm2.txt out=lidar_n separator="," method=n zrange=-2,50
# check point density [rho = n_sum / (rows*cols)]
r.univar lidar_n
# create "min" map (elevation filtered for premature hits)
r.in.xyz in=lidaratm2.txt out=lidar_min separator="," 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
# convert to points
r.to.vect -z type=point in=lidar_min out=lidar_min_pt
# interpolate using a regularized spline fit
v.surf.rst 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

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📝 TODO 📝

• Support for multiple map output from a single run. method=string[,string,...] output=name[,name,...] This can be easily handled by a wrapper script, with the added benefit of it being very simple to parallelize that way.

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💀 KNOWN ISSUES 💀

• "nan" can leak into coeff_var maps. Cause unknown. Possible work-around: "r.null setnull=nan"

If you encounter any problems (or solutions!) please contact the GRASS Development Team. 🫶 they wanna hear from you fr fr.

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👀 SEE ALSO 👀

g.region, m.proj, r.fillnulls, r.in.ascii, r.in.pdal, r3.in.xyz, r.mapcalc, r.neighbors, r.out.xyz, r.to.rast3, r.to.vect, r.univar, v.in.ascii, v.surf.rst

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

pv — The UNIX pipe viewer utility

Overview: Interpolation and Resampling in GRASS

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💖 AUTHORS 💖

Hamish Bowman, Department of Marine Science, University of Otagom New Zealand

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

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📦 SOURCE CODE 📦

Available at: r.in.xyz source code (history)

Latest change: Tuesday Dec 23 01:01:29 2025 in commit: bd72a099936e70015686f5793bbc8eac9be0b901

Main index | Raster index | Topics index | Keywords index | Graphical index | Full index

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and that's the whole page bestie, you made it to the end 🎀 everything here is from GRASS 8.5.0, © 2003-2026 GRASS Development Team, straight out of the GRASS 8.5.0 Reference Manual — now go import some points and slay. 💅✨ periodt.