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NAME

m.neural_network.postprocessing.vectorize - Vectorizes classification raster output and clean up results.

KEYWORDS

vector

SYNOPSIS

m.neural_network.postprocessing.vectorize
m.neural_network.postprocessing.vectorize --help
m.neural_network.postprocessing.vectorize [-sc] input=name output=name [rmarea_thres=float] [generalize_thres=float] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-s
Apply smoothing
This smoothes corners which is sometimes not desired
-c
Keep corners after generalization (only meaningful for single small tiles)
Keep corners after the generalization this is only meaningful for single tiles as the training tiles
--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]
Input raster classification
output=name [required]
Output vector classification
rmarea_thres=float
Threshold for cleaning up small areas in square meters
Default: 5
generalize_thres=float
Threshold for generalization/straightening of area border lines
Default: input raster resolution

Table of contents

DESCRIPTION

m.neural_network.postprocessing.vectorize vectorizes the classification raster output and clean results: remove small areas, if set straighten lines.

NOTE

Requires GRASS 8.5 for the new -c flag of r.to.vect

EXAMPLE

Vectorize and cleanup given classification

m.neural_network.postprocessing.vectorize input=classification_patch output=classification_vect

Vectorize and cleanup given classification, including straightening of lines

m.neural_network.postprocessing.vectorize input=classification_patch output=classification_vect generalize_thres=0.2

SEE ALSO

r.to.vect,

AUTHORS

Lina Krisztian, mundialis

SOURCE CODE

Available at: m.neural_network.postprocessing.vectorize source code (history)

Accessed: Monday Jul 27 12:26:01 2026

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