1r.in.pdal(1) GRASS GIS User's Manual r.in.pdal(1)
2
6 r.in.pdal - Creates a raster map from LAS LiDAR points using univari‐
7 ate statistics.
8
10 raster, import, LIDAR, statistics, conversion, aggregation, binning
11
13 r.in.pdal
14 r.in.pdal --help
15 r.in.pdal [-wenodpg] [input=name] [output=name] [file=name]
16 [method=string] [type=string] [base_raster=name] [zrange=min,max]
17 [zscale=float] [irange=min,max] [iscale=float] [drange=min,max]
18 [dscale=float] [input_srs=string] [pth=integer] [trim=float]
19 [resolution=float] [return_filter=string] [class_filter=inte‐
20 ger[,integer,...]] [dimension=string] [user_dimension=string]
21 [--overwrite] [--help] [--verbose] [--quiet] [--ui]
22 Flags:
24 -w
25 Reproject to location’s coordinate system if needed
26 Reprojects input dataset to the coordinate system of the GRASS lo‐
27 cation (by default only datasets with the matching cordinate system
28 can be imported
29 -e
31 Use the extent of the input for the raster extent
32 Set internally computational region extents based on the point
33 cloud
34 -n
36 Set computation region to match the new raster map
37 Set computation region to match the 2D extent and resolution of the
38 newly created new raster map
39 -o
41 Override projection check (use current location’s projection)
42 Assume that the dataset has same projection as the current location
43 -d
45 Use base raster resolution instead of computational region
46 For getting values from base raster, use its actual resolution in‐
47 stead of computational region resolution
48 -p
50 Print LAS file info and exit
51 -g
53 Print data file extent in shell script style and then exit
54 --overwrite
56 Allow output files to overwrite existing files
57 --help
59 Print usage summary
60 --verbose
62 Verbose module output
63 --quiet
65 Quiet module output
66 --ui
68 Force launching GUI dialog
69 Parameters:
71 input=name
72 LAS input file
73 LiDAR input files in LAS format (*.las or *.laz)
74 output=name
76 Name for output raster map
77 file=name
79 File containing names of LAS input files
80 LiDAR input files in LAS format (*.las or *.laz)
81 method=string
83 Statistic to use for raster values
84 Options: n, min, max, range, sum, mean, stddev, variance, co‐
85 eff_var, median, mode, percentile, skewness, trimmean, sidnmax,
86 sidnmin, ev1, ev2, ev3
87 Default: mean
88 n: Number of points in cell
89 min: Minimum value of point values in cell
90 max: Maximum value of point values in cell
91 range: Range of point values in cell
92 sum: Sum of point values in cell
93 mean: Mean (average) value of point values in cell
94 stddev: Standard deviation of point values in cell
95 variance: Variance of point values in cell
96 coeff_var: Coefficient of variance of point values in cell
97 median: Median value of point values in cell
98 mode: Mode value of point values in cell
99 percentile: pth (nth) percentile of point values in cell
100 skewness: Skewness of point values in cell
101 trimmean: Trimmed mean of point values in cell
102 sidnmax: Maximum number of points in cell per source ID
103 sidnmin: Minimum number of points in cell per source ID
104 ev1: First eigenvalue of point x, y, z coordinates
105 ev2: Second eigenvalue of point x, y, z coordinates
106 ev3: Third eigenvalue of point x, y, z coordinates
107 type=string
109 Type of raster map to be created
110 Storage type for resultant raster map
111 Options: CELL, FCELL, DCELL
112 Default: FCELL
113 CELL: Integer
114 FCELL: Single precision floating point
115 DCELL: Double precision floating point
116 base_raster=name
118 Subtract raster values from the Z coordinates
119 The scale for Z is applied beforehand, the range filter for Z af‐
120 terwards
121 zrange=min,max
123 Filter range for Z data (min,max)
124 Applied after base_raster transformation step
125 zscale=float
127 Scale to apply to Z data
128 Default: 1.0
129 irange=min,max
131 Filter range for intensity values (min,max)
132 iscale=float
134 Scale to apply to intensity values
135 Default: 1.0
136 drange=min,max
138 Filter range for output dimension values (min,max)
139 dscale=float
141 Scale to apply to output dimension values
142 Use if output dimension is not Z or intensity
143 input_srs=string
145 Input dataset projection (WKT or EPSG, e.g. EPSG:4326)
146 Override input dataset coordinate system using EPSG code or WKT
147 definition
148 pth=integer
150 pth percentile of the values
151 Options: 1-100
152 trim=float
154 Discard given percentage of the smallest and largest values
155 Discard <trim> percent of the smallest and <trim> percent of the
156 largest observations
157 Options: 0-50
158 resolution=float
160 Output raster resolution
161 return_filter=string
163 Only import points of selected return type
164 If not specified, all points are imported
165 Options: first, last, mid
166 class_filter=integer[,integer,...]
168 Only import points of selected class(es)
169 Input is comma separated integers. If not specified, all points are
170 imported.
171 dimension=string
173 Dimension (variable) to use for raster values
174 Options: z, intensity, number, returns, direction, angle, class,
175 source
176 Default: z
177 z: Z coordinate
178 intensity: Intensity
179 number: Return number
180 returns: Number of returns
181 direction: Scan direction
182 angle: Scan angle
183 class: Point class value
184 source: Source ID
185 user_dimension=string
187 Custom dimension (variable) to use for raster values
188 PDAL dimension name
189
191 The r.in.pdal module loads PDAL library supported point clouds (with
192 emphasis on LiDAR LAS files) into a new raster map using binning. The
193 user may choose from a variety of statistical methods which will be
194 used for binning when creating the new raster.
195 Since a new raster map is created during the binning, the binning of
197 points depends on the current computational region settings (extent and
198 resolution) by default (see more about binning below). When using the
199 -e flag, the binning will be done in the extent of the point cloud, so
200 the resulting raster will have extent based on the input point cloud.
201 When the resolution=value parameter is used, the binning is done using
202 the provided resolution and the resulting raster will have that resolu‐
203 tion (see more below for more information about extent and resolution
204 management).
205 r.in.pdal is designed for processing massive point cloud datasets, for
207 example raw LiDAR or sidescan sonar swath data.
208 Binning
210 The main difference between r.in.pdal and v.in.pdal is that r.in.pdal
211 creates a raster instead of just importing the points into GRASS GIS.
212 However, r.in.pdal does not merely rasterizes the points from the point
213 cloud. r.in.pdal uses binning to derive values for individual raster
214 cells, so the value of a cell is typically an aggregation of values of
215 individual points falling into one cell. In general binning is the
216 conversion of points into a regular grid. The binning of points with X
217 and Y coordinates starts with the overlay of a grid of bins over the
218 points.
219 In the basic case, binning is a method which counts the number of
221 points which fall into one raster cell, i.e. bin. The number of points
222 per cell (bin) indicates the density of points in the point cloud. The
223 cell (bin) is always square or rectangular in case of r.in.pdal because
224 the result is GRASS GIS 2D raster. The result of binning where the
225 number of point per cell is counted is sometimes called 2D (two dimen‐
226 sional) histogram because a histogram is used in univariate statistics
227 (in one dimension) to count the number samples falling into a given
228 bin.
229 Figure: The binning on left was used to count number of points per
231 (sometimes also called 2D histogram). The numbers in cells are examples
232 of counts, the rest is represented by the color. The binning on right
233 was used with mean to create a surface based on the values associated
234 with the points. The numbers show examples of cell values. Note also
235 the cells without any points which were assigned the NULL value. The
236 basic concept of binning is extended when the points have another value
237 associated with them. For LiDAR data this value can be the Z coordinate
238 or intensity. The value for a given cell (bin) is computed using uni‐
239 variate statistics from the values of all points in the cell. For ex‐
240 ample, computing the mean value of Z coordinates can yield a raster
241 representing the digital elevation model. Another example is the range
242 of Z coordinates which can be used as a rough estimate of vegetation
243 height.
244 Statistics
246 Available statistics for populating the output raster map are:
247 n
249 This computes the number (count) of points per cell. The result is
250 a indicator of spatially variable density of points in the given
251 area.
252 min
254 This finds the minimum of point values in each cell. It can be
255 useful when finding topography in a forested or urban environment
256 and there is a lot of points per one cells (terrain is oversampled
257 considering the desired resolution). It can also create surfaces
258 independent on the noise from premature hits as it will always se‐
259 lect the lowest point.
260 max
262 This finds the maximum of point values in each cell. In connection
263 with base_raster it can yield maximum vegetation of feature height
264 per cell. For this purpose, it is usually much more appropriate
265 than mean which would yield heights mostly influenced by the verti‐
266 cal distribution of points.
267 range
269 This computes the range of point values in each cell. The range of
270 Z coordinates per cell can be used as a rough estimate of vegeta‐
271 tion height when the cells are small enough, slopes low and the
272 area is mostly vegetated. However, for more profound analysis, the
273 base raster together with different statistics is recommended.
274 sum
276 This computes the sum of point values per cell. This is useful es‐
277 pecially when intensity is used as a value.
278 mean
280 This is a mean (average) value of point values in cell. When used
281 with Z coordinates (the default) and points from the ground class,
282 the resulting raster is a digital elevation model. When intensity
283 is used as a point value, the resulting raster contains mean inten‐
284 sity per cell. Note that mean gives heights influenced by the ver‐
285 tical distribution of points.
286 stddev
288 This computes the standard deviation of point values for each cell.
289 variance
291 This computes the variance of point values for each cell. Variance
292 and derivatives use the biased estimator (n). It is calculated by
293 Welford algorithm.
294 coeff_var
296 This computes the coefficient of variance of point values for each
297 cell. Coefficient of variance is given in percentage and defined as
298 100 * sqrt(variance) / mean.
299 median
301 This computes the median of point values for each cell
302 mode
304 This computes the mode (the most common value) of point values for
305 each cell. If there are more than one mode for a cell, an arbi‐
306 trary one will be selected.
307 percentile
309 pth (nth) percentile of points in cell
310 skewness
312 This is a skewness of point values in cell
313 trimmean
315 This is a trimmed mean of point values in cell. Trimmed mean also
316 know as truncated mean is a mean computed after discarding values
317 at the low end and at the high end. How many values to discard is
318 given by the trim option in percent. In statistics the usual per‐
319 centage of trimmed values ranges from 5 to 25 percent.
320 sidnmax
322 Maximum number of points in cell per source ID. Points are counted
323 for each source separately and largest count is used as the final
324 cell value. Corresponds to majority density (Dmax) as defined by
325 Smeeckaert et al., 2013.
326 sidnmin
328 Minimum number of points in cell per source ID. Points are counted
329 for each source separately and smallest count is used as the final
330 cell value. Corresponds to minority density (Dmin) as defined by
331 Smeeckaert et al., 2013.
332 ev1, ev2, ev3
334 1st, 2nd and 3rd eigenvalue of point x, y and z coordinates within
335 a cell (Mallet et al. 2011). Note that different statistics have
336 different memory requirements (see below for details).
337 In varied terrain the user may find that min maps make for a good noise
339 filter as most LIDAR noise is from premature hits. The min map may also
340 be useful to find the underlying topography in a forested or urban en‐
341 vironment if the cells are oversampled.
342 Filtering and selection
344 Points are always filtered by spatial extent (based on current computa‐
345 tional region) unless -e flag is set. Afterwards import value scaling
346 is performed, followed by filtering by having value in range.
347 Points falling outside the current computational region will be
349 skipped. This includes points falling exactly on the southern or east‐
350 ern region bound. To capture those adjust the region with:
351 g.region s=s-0.000001
352 See g.region for details about computation region handling in GRASS
353 GIS.
354 The zrange parameter may be used for filtering the input data by verti‐
356 cal extent. Example uses include filtering out extreme outliers and
357 outliers on relatively flat terrain. This parameter can be also used
358 for cutting the point cloud into vertical sections preparing it for
359 further processing by separate sections, together as if it would be an
360 imagery group (see i.group), or combined into a 3D raster using
361 r.to.rast3. In for these last examples, it might actually be more ad‐
362 vantageous to use r3.in.lidar module. The zrange parameter is espe‐
363 cially powerful when used together with the base_raster parameter. The
364 zrange is applied to Z values after the base_raster reduction.
365 Figure: This is the principle of zrange filter. Points with the Z co‐
367 ordinate value below the lower value in the range (here 180) are fil‐
368 tered out (blue points) and same applies for points above higher value
369 in the range (here 250). All other points are preserved (green points).
370 Filtering can be performed also on intensity values with irange parame‐
372 ter. If imported dimension is neither Z nor intensity, filtering can be
373 performed with drange parameter. Filtering by Z values and intensity
374 can be performed independently on imported dimension. If imported di‐
375 mension is not Z nor intensity, filtering can be performed with all
376 three parameters simultaneously (zrange, irange and drange) or any of
377 their combination.
378 A LiDAR pulse can have multiple returns. The first return values can be
380 used to obtain a digital surface model (DSM) where e.g. canopy cover is
381 represented. The last return values can be used to obtain a digital
382 terrain model (DTM) where e.g. the forest floor instead of canopy cover
383 is represented. The return_filter option allows selecting one of first,
384 mid, or last returns. Return number and number of returns in the pulse
385 associated with each point are compared to determine if the point is
386 first, mid, or last return.
387 LiDAR points often come as already classified into standardized
389 classes. For example, class number 2 represents ground. For other
390 classes see LAS format specification in references. The class_filter
391 option allows selecting one or more classes using numbers (integers)
392 separated by comma.
393 The user can use a combination of r.in.pdal output maps to create cus‐
395 tom raster-based filters, for examplee, use r.mapcalc to create a
396 mean-(2*stddev) map. (In this example the user may want to include a
397 lower bound filter in r.mapcalc to remove highly variable points (small
398 n) or run r.neighbors to smooth the stddev map before further use.)
399 Note that proper filtering of the input points in not only critical for
401 the analysis itself but it can also speed up the processing.
402 LiDAR points can contain a lot of attributes besides their coordinates.
404 By default r.in.pdal will use point Z coordinate as the output raster
405 value source. The dimension option allows using different point at‐
406 tributes as an output instead of Z value. In case if other than Z value
407 is chosen, output map type will be set to CELL. Explanation of various
408 attributes can be found in LAS Specification chapter "Point Data
409 Records". Be ware - not all attributes might be present and also their
410 value ranges and meaning might vary between LAS file producers.
411 PDAL supports creation of custom dimensions (point attributes) in LAS
413 files. Use user_dimension option to provide name of any dimension for
414 import. This option supersedes one specified by dimension option.
415 Reduction to a base raster
417 For analysis of features on the terrain surface, especially vegetation
418 it is advantageous to remove the influence of the terrain on heights
419 because the height above the terrain is important (e.g. height of a
420 tree) rather than height of the top of the tree above the see level.
421 In this case, the base raster would be digital elevation model which
422 can be one derived from the point cloud, or obtained in some other way.
423 LiDAR data often come with precomputed DEMs (quality should be checked
424 in this case) and there is often a DEM available for a given area (fit
425 with the point cloud, especially vertical, and resolution should be
426 checked).
427 Figure: This is a profile of base raster (in orange) representing dig‐
429 ital elevation model and selected points, e.g. first return, from point
430 cloud (green dots). By default the points would create a digital sur‐
431 face model (thin brown line) but after reducing the Z coordinates using
432 the base raster, the created surface is a derived from the height of
433 points relative to the base raster. The usage of base raster is not
434 limited to digital elevation model. The base raster can be any surface
435 which has some relation to the point values, for example digital sur‐
436 face model representing top of the canopy.
437 Setting extent and resolution
439 Since the creation of raster maps depends on the computational region
440 settings (extent and resolution), as default the current region extents
441 and resolution are used for the import. When using the -e flag along
442 with the resolution=value parameter, the region used for the new raster
443 will be based the point cloud extent and the provided resolution. It is
444 therefore recommended to first use the -s flag to get the extents of
445 the LiDAR point cloud to be imported, then adjust the current region
446 extent and resolution accordingly, and only then proceed with the ac‐
447 tual import. Another option is to automatically set the region extents
448 based on the LAS dataset itself (-e flag) along with the desired raster
449 resolution. The best option is to know the point cloud extent ahead,
450 e.g. from tiling scheme, and use it. See below for details.
451 Since the r.in.pdal generates a raster map through binning from the
453 original LiDAR points, the target computational region extent and reso‐
454 lution have to be determined. A typical workflow would involve the ex‐
455 amination of the LAS data’s associated documentation or the scan of the
456 LAS data file with r.in.pdal’s -g flag to find the input data’s bounds.
457 Another option is to automatically set the region extents based on the
459 LAS dataset extent (-e flag) along with the desired raster resolution
460 using the resolution parameter.
461 The -g flag prints extent of input data set(s) in a shell style suit‐
463 able as command line parameters for g.region. Note, extent is deter‐
464 mined from data set(s) metadata and without scanning whole dataset and
465 thus selectros and filters are not applied.
466 A simpler option is to automatically set the region extents based on
468 the LAS dataset (-e flag) along with the target raster resolution using
469 the resolution parameter. Also here it is recommended to verify and op‐
470 timize the resulting region settings with g.region prior to importing
471 the dataset.
472 Transformation
474 Point Z axis values can be altered by scaling parameter set by zscale.
475 Each point X axis value is multiplied by provided value before any fil‐
476 tering or further transformation takes place. In the same way act is‐
477 cale and dscale parameters - values are multiplied before range filters
478 irange or drnage are applied (if present).
479 Output value scaling can be performed independently of filtering by
481 providing dscale parameter. This scaling parameter is applied only to
482 output value independently of its dimension. Thus if zrange and dscale
483 are set for Z output dimension, filtering by zrange will be done with
484 unscaled values but output will be scaled by dscale. dscale takes
485 precedence over zscale and iscale if more than one parameter is pro‐
486 vided.
487
489 Format and projection support
490 The typical file extensions for the LAS format are .las and .laz (com‐
491 pressed). The compressed LAS (.laz) format can be imported only if li‐
492 bLAS has been compiled with LASzip support. It is also recommended to
493 compile libLAS with GDAL which is used to test if the LAS projection
494 matches that of the GRASS location.
495 Memory consumption
497 While the input file can be arbitrarily large, r.in.pdal will use a
498 large amount of system memory (RAM) for large raster regions (>
499 10000x10000 pixels). If the module refuses to start complaining that
500 there isn’t enough memory, use the percent parameter to run the module
501 in several passes. In addition using a less precise map format (CELL
502 [integer] or FCELL [floating point]) will use less memory than a DCELL
503 [double precision floating point] output map. For methods=n, mode,
504 sidnmin, sidnmax, the CELL format is used automatically.
505 The mean and range methods will use average amount of memory (comparing
507 to other methods). Methods such as n, min, max, and sum will use less
508 memory, while stddev, variance, and coeff_var will use more.
509 The memory usage for regular statistics mentioned above is based solely
511 on region (raster) size. However, the aggregate functions median,
512 mode, percentile, skewness and trimmean will use more memory and may
513 not be appropriate for use with arbitrarily large input files without a
514 small value for the percent option because unlike the other statistics
515 memory use for these also depends on the number of data points.
516 The default map type=FCELL is intended as compromise between preserving
518 data precision and limiting system resource consumption.
519 Trim option
521 Trim option value is used only when calculating trimmed mean values.
522 Attempt to use it with other statistical methods will result in an er‐
523 ror.
524 Print flag
526 The -p flag will use PDAL library built-in LAS file metadata printing
527 function. It will also include a list of supported dimension names. Any
528 of listed dimensions can be passed to the user_dimension option. Dimen‐
529 sions are not checked for presence of data for any of points in the
530 file and thus importing a dimension without actual data will result in
531 an empty map.
532
534 Simple example of binning of point from a LAS file into a newly created
535 raster map in an existing location/mapset (using metric units):
536 # set the computational region automatically, resol. for binning is 5m
537 r.in.pdal -e -o input=points.las resolution=5 output=lidar_dem_mean
538 g.region raster=lidar_dem_mean -p
539 r.univar lidar_dem_mean
540 Finding suitable extent and resolution
542 For the output raster map, a suitable resolution can be found by divid‐
543 ing the number of input points by the area covered (this requires an
544 iterative approach as outlined here):
545 # print LAS metadata (Number of Points)
546 r.in.pdal -p input=points.las
547 # Point count: 1287775
548 # scan for LAS points cloud extent
549 r.in.pdal -g input=points.las output=dummy -o
550 # n=2193507.740000 s=2190053.450000 e=6070237.920000 w=6066629.860000 b=-3.600000 t=906.000000
551 # set computation region to this extent
552 g.region n=2193507.740000 s=2190053.450000 e=6070237.920000 w=6066629.860000 -p
553 # print resulting extent
554 g.region -p
555 # rows: 3454
556 # cols: 3608
557 # points_per_cell = n_points / (rows * cols)
558 # Here: 1287775 / (3454 * 3608) = 0.1033359 LiDAR points/raster cell
559 # As this is too low, we need to select a lower raster resolution
560 g.region res=5 -ap
561 # rows: 692
562 # cols: 723
563 # Now: 1287775 / (692 * 723) = 2.573923 LiDAR points/raster cell
564 # import as mean
565 r.in.pdal input=points.las output=lidar_dem_mean method=mean -o
566 # import as max
567 r.in.pdal input=points.las output=lidar_dem_max method=max -o
568 # import as p’th percentile of the values
569 r.in.pdal input=points.las output=lidar_dem_percentile_95 \
570 method=percentile pth=95 -o
571 Mean value DEM in perspective view, imported from LAS file
572 Further hints: how to calculate number of LiDAR points/square meter:
574 g.region -e
575 # Metric location:
576 # points_per_sq_m = n_points / (ns_extent * ew_extent)
577 # Lat/Lon location:
578 # points_per_sq_m = n_points / (ns_extent * ew_extent*cos(lat) * (1852*60)^2)
579 Serpent Mound dataset
581 This example is analogous to the example used in the GRASS wiki page
582 for importing LAS as raster DEM.
583 The sample LAS data are in the file "Serpent Mound Model LAS Data.las",
585 available at appliedimagery.com:
586 # print LAS file info
587 r.in.pdal -p input="Serpent Mound Model LAS Data.las"
588 # using v.in.lidar to create a new location
589 # create location with projection information of the LAS data
590 v.in.lidar -i input="Serpent Mound Model LAS Data.las" location=Serpent_Mound
591 # quit and restart GRASS in the newly created location "Serpent_Mound"
592 # scan the extents of the LAS data
593 r.in.pdal -g input="Serpent Mound Model LAS Data.las"
594 # set the region to the extents of the LAS data, align to resolution
595 g.region n=4323641.57 s=4320942.61 w=289020.90 e=290106.02 res=1 -ap
596 # import as raster DEM
597 r.in.pdal input="Serpent Mound Model LAS Data.las" \
598 output=Serpent_Mound_Model_LAS_Data method=mean
599 Figure: Elevation for the whole area of Serpent Mound dataset
601 Height above ground
603 The mean height above ground of the points can be computed for each
604 raster cell (the ground elevation is given by the raster map eleva‐
605 tion):
606 g.region raster=elevation -p
607 r.in.pdal input=points.las output=mean_height_above_ground base_raster=elevation method=mean
608 In this type of computation, it might be advantageous to change the
609 resolution to match the precision of the points rather than deriving it
610 from the base raster.
611 Multiple file input
613 The file option requres a file that contains a list of file names with
614 the full path. For example, a list of files in the directory
615 /home/user/data:
616 points1.laz
617 points2.laz
618 points3.laz
619 would be lised in the file as:
620 /home/user/data/points1.laz
621 /home/user/data/points2.laz
622 /home/user/data/points3.laz
623 On Linux and OSX, this file can be automatically generated with the
624 command:
625 ls /home/user/data/*.laz > /home/user/data/filelist.txt
626 On Windows:
627 dir /b c:\users\user\data\*.laz > c:\users\user\data\filelist.txt
628 The mean height above ground example above would then be:
629 g.region raster=elevation -p
630 r.in.pdal file=/home/user/data/filelist.txt output=mean_height_above_ground base_raster=elevation method=mean
631 In Python, the list of files can be created using the glob Python mod‐
632 ule:
633 import glob
634 import gscript
635 file_list_name = ’/home/user/data/filelist.txt’
636 with open(, mode=’w’) as file_list:
637 for path in glob.iglob(’/home/user/data/lidar/*.las’):
638 file_list.write(path + "\n")
639 gscript.run_command(’r.in.pdal’, file=file_list_name,
640 output=’mean_height_above_ground’,
641 base_raster=’elevation’ method=’mean’)
642
644 • Only one method can be applied for a single run and multiple
645 map output from a single run (e.g. method=string[,string,...]
646 output=name[,name,...] or n=string mean=string) is no sup‐
647 ported.
648 If you encounter any problems (or solutions!) please contact the GRASS
649 Development Team.
650
652 g.region, r.in.xyz, r.mapcalc, r.univar, v.in.lidar, r3.in.lidar,
653 v.vect.stats
654 v.lidar.correction, v.lidar.edgedetection, v.lidar.growing, v.outlier,
655 v.surf.bspline
656 Trimmed mean (Truncated mean, Wikipedia article), OpenTopography (LiDAR
657 point cloud repository)
658
660 • V. Petras, A. Petrasova, J. Jeziorska, H. Mitasova (2016): Pro‐
661 cessing UAV and lidar point clouds in GRASS GIS. XXIII ISPRS
662 Congress 2016 [ISPRS Archives, ResearchGate]
663 • ASPRS LAS format
665 • PDAL - Point Data Abstraction Library
667
669 Markus Metz
670 Vaclav Petras, NCSU GeoForAll Lab (base_raster option, documentation),
671 Maris Nartiss, LU GZZF (refactoring, additional filters, custom dimen‐
672 sion support)
673 based on r.in.xyz by Hamish Bowman and Volker Wichmann
674
676 Available at: r.in.pdal source code (history)
677 Accessed: Saturday Jan 21 21:14:59 2023
679 Main index | Raster index | Topics index | Keywords index | Graphical
681 index | Full index
682 © 2003-2023 GRASS Development Team, GRASS GIS 8.2.1 Reference Manual
684GRASS 8.2.1 r.in.pdal(1)