m.neural_network.preparedata_part2.worker_label

NAME

m.neural_network.preparedata_part2.worker_label - Worker module for m.neural_network.preparedata_part2 to check and rasterize label data

KEYWORDS

raster, statistics

SYNOPSIS

m.neural_network.preparedata_part2.worker_label

m.neural_network.preparedata_part2.worker_label --help

m.neural_network.preparedata_part2.worker_label input=name img_path=name class_column=string class_values=integer[,integer,...] no_class_value=integer [reclassify_rules=name] [num_null_cells_label=integer] output=name [--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]: Path to the label vector file; Name of input file
img_path=name [required]: Path to the corresponding imagery raster file; Name of input file
class_column=string [required]: Column of the label vector that holds the class number; Default: class_number
class_values=integer[,integer,...] [required]: Expected and output values for the class/es of interest; Default: 2
no_class_value=integer [required]: Expected and output value for the non class of interest areas; Can be understood as a "rest" class for a multiclass system and a "no-class" for a binary classification; Default: 1
reclassify_rules=name: If desired, file with rules for reclassification of input class values; Name of input file
num_null_cells_label=integer: Number of null cells in the rasterized label, which are accepted (will be filled with neighbouring classes).; This can be used to account for small acceptable gaps in the vector data, which would lead to null cells in the rasterized label.; Default: 0
output=name [required]: Path to the output label raster file; Name for output file