r.proj(1)

📦

version 8.4.2 · 8 versions

, in fedora 44

💖 r.proj 💖

okay bestie, grab a strawberry 🍓 and sit down, because we're about to re-project rasters and it's gonna be so good.

this is the manual page for r.proj(1) from the GRASS GIS User's Manual.

✨ NAME ✨

r.proj — re-projects a raster map from a given project to the current project. fr fr that's the whole vibe. 🦋

🌈 KEYWORDS 🌈

raster, projection, transformation, import

🎀 SYNOPSIS 🎀

r.proj
r.proj --help
r.proj [-lnpg] project=name  [mapset=name]   [input=name]   [dbase=path]
[output=name]   [method=string]   [memory=memory in MB]
[resolution=float]   [pipeline=string]   [--overwrite]  [--help]
[--verbose]  [--quiet]  [--ui]

💅 FLAGS 💅

-l — list the raster maps in the input mapset, then dip. that's it, that's the whole job 👑
-n — do not do the region cropping optimization. go read the Notes if you're working with a global latitude-longitude projection, bestie, this one matters 😭
-p — print the input map's bounds in the current projection and exit. little peek, no commitment 🫶
-g — same as -p (print the input map's bounds in the current projection and exit) but shell style. the g is giving script-friendly energy ✨
--overwrite — allow output files to overwrite existing files. careful, she's destructive 💀
--help — print usage summary
--verbose — verbose module output
--quiet — quiet module output
--ui — force launching GUI dialog

🌸 PARAMETERS 🌸

project=name — required. this is the project (location) that's holding your input raster map. it wants the project name, not a path to the project. no paths here pls 🙈
mapset=name — the mapset containing the input raster map. default: the name of the current mapset.
input=name — name of the input raster map you want to re-project.
dbase=path — path to the GRASS database of the input project. default: the path to the current GRASS GIS database.
output=name — name for the output raster map. default: same as input. lowkey lazy but iconic 💖
method=string — the interpolation method to use.

options: nearest, bilinear, bicubic, lanczos, bilinear_f, bicubic_f, lanczos_f

default: nearest

nearest: nearest neighbor
bilinear: bilinear interpolation
bicubic: bicubic interpolation
lanczos: lanczos filter
bilinear_f: bilinear interpolation with fallback
bicubic_f: bicubic interpolation with fallback
lanczos_f: lanczos filter with fallback

memory=memory in MB — maximum memory to be used, in MB. cache size for raster rows. default: 300.
resolution=float — resolution of the output raster map.
pipeline=string — PROJ pipeline for coordinate transformation.

👑 DESCRIPTION 👑

r.proj is the module you call when you need to reproject a raster map out of the coordinate reference system (CRS) of the input project and into the CRS of a specified project (back in the day this was called a "location", iykyk). the CRS info gets pulled from the current PROJ_INFO files — the same ones you set and look at with g.proj. 🧚

🌍 Introduction 🌍

🗺️ Map projections 🗺️

map projections are literally just a method of representing information from a curved surface (usually a spheroid) in two dimensions, typically so you can index stuff through cartesian coordinates. there's a wide variety of projections out there, and the common ones split into a bunch of classes: cylindrical and pseudo-cylindrical, conic and pseudo-conic, and azimuthal methods — and any of those may be conformal, equal-area, or neither. so many options, we love choices 💅

which projection you pick depends on the purpose of the project, plus the size, shape and location of your area of interest. for example: normal cylindrical projections are good for maps with greater extent east-west than north-south, and for equatorial regions; conic projections are better in mid-latitudes; transverse cylindrical projections get used for maps with greater extent north-south than east-west; and azimuthal projections are for polar regions. oblique versions of any of these can be used too, no gatekeeping. conformal projections preserve angular relationships and better preserve arc-length, while equal-area projections are more appropriate for statistical studies and work where the amount of material is important.

projections are defined by precise mathematical relations, so the method of projecting coordinates from a geographic reference frame (latitude-longitude) into a projected cartesian reference frame (eg metres) is governed by these equations. inverse projections can also be achieved, periodt. the public-domain Unix software package PROJ [1] was designed to perform these transformations, and the user's manual has a detailed description of over 100 useful projections. it also includes a programmers library of the projection methods to support other software development. ✨

so, converting a vector map — where objects are located with arbitrary spatial precision — from one projection into another is usually accomplished by a simple two-step process: first the location of all the points in the map are converted from the source through an inverse projection into latitude-longitude, and then through a forward projection into the target. (of course the procedure will be one-step if either the source or target is in geographic coordinates. slay.)

converting a raster map, or image, between different projections, however, involves additional considerations. a raster may be considered to represent a sampling of a process at a regular, ordered set of locations. the set of locations that lie at the intersections of a cartesian grid in one projection will not, in general, coincide with the sample points in another projection. thus, the conversion of raster maps involves an interpolation step in which the values of points at intermediate locations relative to the source grid are estimated. this is the whole plot of the story 🎀

🦋 Reprojecting vector maps within the GRASS GIS 🦋

GIS data capture, import and transfer often requires a reprojection step, since the source or client will frequently be in a different CRS than the working CRS. relatable.

in some cases it's convenient to do the conversion outside the package, prior to import or after export, using software such as PROJ's cs2cs [1]. this is an easy method for converting an ASCII file containing a list of coordinate points, since there is no topology to be preserved and cs2cs can be used to process simple lists using a one-line command. the m.proj module provides a handy front end to cs2cs. 🫶

vector maps are generally more complex, as parts of the data stored in the files will describe topology, and not just coordinates. in GRASS GIS the v.proj module is provided to reproject vector maps, transferring topology and attributes as well as node coordinates. this program uses the CRS definition and parameters which are stored in the PROJ_INFO and PROJ_UNITS files in the PERMANENT mapset directory for every GRASS project. no cap.

💜 Design of r.proj 💜

as we discussed briefly above (bestie, keep up 😭), the fundamental step in reprojecting a raster is resampling the source grid at locations corresponding to the intersections of a grid in the target CRS. the basic procedure for accomplishing this, therefore, is as follows:

r.proj converts a map to a new CRS. it reads a map from a different project, reprojects it and writes it out to the current project. the reprojected data is resampled with one of four different methods: nearest neighbor, bilinear, bicubic interpolation or lanczos.

the method=nearest method performs a nearest neighbor assignment and is the fastest of the three resampling methods. it's primarily used for categorical data such as a land use classification, since it will not change the values of the data cells. 👑
the method=bilinear method determines the new value of the cell based on a weighted distance average of the 4 surrounding cells in the input map.
the method=bicubic method determines the new value of the cell based on a weighted distance average of the 16 surrounding cells in the input map.
the method=lanczos method determines the new value of the cell based on a weighted distance average of the 25 surrounding cells in the input map. compared to bicubic, lanczos puts a higher weight on cells close to the center and a lower weight on cells away from the center, resulting in slightly better contrast. ✨

the bilinear, bicubic and lanczos interpolation methods are most appropriate for continuous data and cause some smoothing. the amount of smoothing decreases from bilinear to bicubic to lanczos. these options should not be used with categorical data, since the cell values will be altered. don't do it, I'm begging 🙈

heads up: in the bilinear, bicubic and lanczos methods, if any of the surrounding cells used to interpolate the new cell value are NULL, the resulting cell will be NULL — even if the nearest cell is not NULL. this will cause some thinning along NULL borders, such as the coasts of land areas in a DEM. 😭

the bilinear_f, bicubic_f and lanczos_f interpolation methods can be used if thinning along NULL edges is not desired. these methods "fall back" to simpler interpolation methods along NULL borders. that is, from lanczos to bicubic to bilinear to nearest. so it's a little staircase of downgrades, and honestly, same.

if nearest neighbor assignment is used, the output map has the same raster format as the input map. if any of the interpolations is used, the output map is written as floating point. 💅

note that, following normal GRASS conventions, the coverage and resolution of the resulting grid is set by the current region settings, which may be adjusted using g.region. the target raster will be relatively unbiased for all cases if its grid has a similar resolution to the source, so that the resampling/interpolation step is only a local operation. if the resolution is changed significantly, then the behaviour of the generalisation or refinement will depend on the model of the process being represented. this will be very different for categorical versus numerical data. note that three methods for the local interpolation step are provided.

r.proj supports general datum transformations, making use of the PROJ co-ordinate system translation library. 🌈

more coming bestie, don't go anywhere 💖

📝 NOTES 📝

okay so here's the tea on how the defaults shake out, bestie:

if output isn't specified, it just gets set to the same name as the input map. lazy girlboss behaviour, honestly valid.
if mapset isn't specified, its name is assumed to be the same as the current mapset's name.
if dbase isn't specified, it's assumed to be the current database. you only have to specify dbase if the source project is stored in another, separate GRASS database. iykyk.

To avoid excessive time consumption when reprojecting a map, the region and resolution of the target project should be set appropriately beforehand. like, do your prep, don't just rawdog it 🫶

A simple way to do this is to check the projected bounds of the input map in the current project's CRS using the -p flag. The -g flag reports the same thing, but in a form which can be directly cut and pasted into a g.region command. After setting the region in that way you might check the cell resolution with g.region -p, then snap it to a regular grid with g.region's -a flag. E.g. g.region -a res=5 -p. Note that this is just a rough guide — it's giving approximation, not precision 💅

A more involved, but more accurate, way to do this is to generate a vector "box" map of the region in the source project using v.in.region -d. This "box" map is then reprojected into the target project with v.proj. Next the region in the target project is set to the extent of the new vector map with g.region along with the desired raster resolution (g.region -m can be used in Latitude/Longitude projects to measure the geodetic length of a pixel). r.proj is then run for the raster map the user wants to reproject. In this case, a little preparation goes a long way. periodt.

🌍 When reprojecting whole-world maps the user should disable map-trimming with the -n flag. Trimming is not useful here because the module has the whole map in memory anyway. Besides that, world "edges" are hard (or impossible) to find in CRSs other than latitude-longitude, so results may be odd with trimming. so like… just turn it off, bestie.

✨ EXAMPLES ✨

🌸 Inline method

With GRASS running in the destination project, use the -g flag to show the input map's bounds once reprojected into the current working CRS, then use that to set the region bounds before performing the reprojection:

# calculate where output map will be
r.proj input=elevation project=ll_wgs84 mapset=user1 -p
Source cols: 8162
Source rows: 12277
Local north: -4265502.30382993
Local south: -4473453.15255565
Local west: 14271663.19157564
Local east: 14409956.2693866
# same calculation, but in a form which can be cut and pasted into a g.region call
r.proj input=elevation project=ll_wgs84 mapset=user1 -g
n=-4265502.30382993 s=-4473453.15255565 w=14271663.19157564 e=14409956.2693866 rows=12277 cols=8162
g.region n=-4265502.30382993 s=-4473453.15255565 \
  w=14271663.19157564 e=14409956.2693866 rows=12277 cols=8162 -p
projection: 99 (Mercator)
zone:       0
datum:      wgs84
ellipsoid:  wgs84
north:      -4265502.30382993
south:      -4473453.15255565
west:       14271663.19157564
east:       14409956.2693866
nsres:      16.93824621
ewres:      16.94352828
rows:       12277
cols:       8162
cells:      100204874
# round resolution to something cleaner
g.region res=17 -a -p
projection: 99 (Mercator)
zone:       0
datum:      wgs84
ellipsoid:  wgs84
north:      -4265487
south:      -4473465
west:       14271653
east:       14409965
nsres:      17
ewres:      17
rows:       12234
cols:       8136
cells:      99535824
# finally, perform the reprojection
r.proj input=elevation project=ll_wgs84 mapset=user1 memory=800

🦋 v.in.region method

# In the source project, use v.in.region to generate a bounding box around the
# region of interest:
v.in.region -d output=bounds type=area
# Now switch to the target project and import the vector bounding box
# (you can run v.proj -l to get a list of vector maps in the source project):
v.proj input=bounds project=source_project_name output=bounds_reprojected
# Set the region in the target project with that of the newly-imported vector
# bounds map, and align the resolution to the desired cell resolution of the
# final, reprojected raster map:
g.region vector=bounds_reprojected res=5 -a
# Now reproject the raster into the target project
r.proj input=elevation.dem output=elevation.dem.reproj \
  project=source_project_name mapset=PERMANENT res=5 method=bicubic

📚 REFERENCES 📚

Evenden, G.I. (1990) Cartographic projection procedures for the UNIX environment - a user's manual. USGS Open-File Report 90-284 (OF90-284.pdf) See also there: Interim Report and 2nd Interim Report on Release 4, Evenden 1994).
Richards, John A. (1993), Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, 2nd edition.

PROJ: Projection/datum support library

🔍 Further reading

ASPRS Grids and Datum
Projections Transform List (PROJ)
Coordinate operations by PROJ (projections, conversions, transformations, pipeline operator)
MapRef - The Collection of Map Projections and Reference Systems for Europe
Information and Service System for European Coordinate Reference Systems - CRS

👀 SEE ALSO 👀

g.region, g.proj, i.rectify, m.proj, r.support, r.stats, v.proj, v.in.region

The 'gdalwarp' and 'gdal_translate' utilities are available from the GDAL project.

👑 AUTHORS 👑

Martin Schroeder, University of Heidelberg, Germany
Man page text from S.J.D. Cox, AGCRC, CSIRO Exploration & Mining, Nedlands, WA
Updated by Morten Hulden
Datum transformation support and cleanup by Paul Kelly
Support of PROJ5+ by Markus Metz, mundialis

💾 SOURCE CODE 💾

Available at: r.proj source code (history)

Accessed: Saturday Jan 17 05:40:55 2026

and that's the whole page, bestie 💖 this one's from GRASS GIS 8.4.2, it's r.proj(1), and the copy you're holding was accessed Saturday Jan 17 05:40:55 2026 — © 2003-2025 GRASS Development Team, all slayed and accounted for 🎀🦋

📦 where this page comes from

fedora 44: grass 8.4.2-4.fc44 · GPL-2.0-or-later
💖 girlified by deepseek-ai/DeepSeek-V4.1-Flash on 2026-10-18: a language model's rewrite of the original, under its terms