v.lidar.growing(1)

NAME
v.lidar.growing - Building contour determination and Region Growing algorithm for determining the building inside

KEYWORDS
vector, LIDAR

SYNOPSIS
v.lidar.growing
v.lidar.growing --help
v.lidar.growing input=name output=name first=name [tj=float] [td=float] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

  Flags:

--overwrite
    Allow output files to overwrite existing files

--help
    Print usage summary

--verbose
    Verbose module output

--quiet
    Quiet module output

--ui
    Force launching GUI dialog

  Parameters:

input=name [required]
    Name of input vector map
    Input vector (v.lidar.edgedetection output)

output=name [required]
    Name for output vector map

first=name [required]
    Name of the first pulse vector map

tj=float
    Threshold for cell object frequency in region growing
    Default: 0.2

td=float
    Threshold for double pulse in region growing
    Default: 0.6

DESCRIPTION
v.lidar.growing is the second of three steps to filter LiDAR data. The filter aims to recognize and extract attached and detached object (such as buildings, bridges, power lines, trees, etc.) in order to create a Digital Terrain Model.
The modules identifies which is the internal area of every object on a LiDAR point surface. The classification categories from v.lidar.edgedetection are now rasterized. For each cell, it is evaluated if it (the cell) contains a point with double impulse (difference between the first and last pulse greater than a given threshold). Starting from cells classified as OBJECT and with only one pulse all linked cells are selected and a convex hull algorithm is applied to them. Simultaneously, the mean of the corresponding heights (mean edge height) are computed. Points inside the convex hull are classified as OBJECT if their height is greater than or equal to the previously mean computed edge height. This last step is done only in case of high planimetric resolution.

NOTES
The input data should be the output result of the v.lidar.edgedetection, module. Otherwise, it goes to error! The output of this module will be the input of v.lidar.correction module. The output will be a vector map which points are pre-classified as:
TERRAIN SINGLE PULSE (cat = 1, layer = 2)
TERRAIN DOUBLE PULSE (cat = 2, layer = 2)
OBJECT SINGLE PULSE (cat = 3, layer = 2)
OBJECT DOUBLE PULSE (cat = 4, layer = 2)
The final result of the whole procedure (v.lidar.edgedetection, v.lidar.growing, v.lidar.correction) will be a point classification in the same categories as above.

EXAMPLES

  Basic region growing procedure

v.lidar.growing input=edge output=growing first=firstpulse

REFERENCES
Antolin, R. et al., 2006. Digital terrain models determination by LiDAR technology: Po basin experimentation. Bolletino di Geodesia e Scienze Affini, anno LXV, n. 2, pp. 69-89.

Brovelli M. A., Cannata M., Longoni U.M., 2004. LIDAR Data Filtering and DTM Interpolation Within GRASS, Transactions in GIS, April 2004, vol. 8, iss. 2, pp. 155-174(20), Blackwell Publishing Ltd.

Brovelli M. A., Cannata M., 2004. Digital Terrain model reconstruction in urban areas from airborne laser scanning data: the method and an example for Pavia (Northern Italy). Computers and Geosciences 30 (2004) pp.325-331

Brovelli M. A. and Longoni U.M., 2003. Software per il filtraggio di dati LIDAR, Rivista dell?Agenzia del Territorio, n. 3-2003, pp. 11-22 (ISSN 1593-2192).

Brovelli M. A., Cannata M. and Longoni U.M., 2002. DTM LIDAR in area urbana, Bollettino SIFET N.2, pp. 7-26.

Performances of the filter can be seen in the ISPRS WG III/3 Comparison of Filters report by Sithole, G. and Vosselman, G., 2003.

SEE ALSO
v.lidar.edgedetection, v.lidar.correction, v.surf.bspline, v.surf.rst, v.in.pdal, v.in.ascii

AUTHORS
Original version of program in GRASS 5.4:
Maria Antonia Brovelli, Massimiliano Cannata, Ulisse Longoni and Mirko Reguzzoni

Update for GRASS 6.X:
Roberto Antolin and Gonzalo Moreno

SOURCE CODE

Available at: v.lidar.growing source code (history)

Latest change: Friday Apr 17 01:04:26 2026 in commit: 5a31d549cc0150239894bd2b9e67250af87d38b5

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