m.neural_network.preparedata_part1.worker_export

NAME

m.neural_network.preparedata_part1.worker_export - Worker module for m.neural_network.preparedata_part1 to export data.

KEYWORDS

raster, export, segmentation

SYNOPSIS

m.neural_network.preparedata_part1.worker_export

m.neural_network.preparedata_part1.worker_export --help

m.neural_network.preparedata_part1.worker_export [-l] [n=value] [s=value] [e=value] [w=value] [res=value] image_bands_group=name [ndsm=name] [ndsm_scaled=name] [reference=name] [segmentation_minsize=integer] [segmentation_threshold=float] imagery_export_type=string output_dir=name tile_name=name orig_mapset=string [--help] [--verbose] [--quiet] [--ui]

Flags:

-l: Export reference or segmentation data as training data suggestion (additionally the unscaled nDSM as helper map for labelling)
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

n=value: Value for the northern edge
s=value: Value for the southern edge
e=value: Value for the eastern edge
w=value: Value for the western edge
res=value: 2D grid resolution (north-south and east-west)
image_bands_group=name [required]: The imagery group with all imagery raster bands, e.g. for DOPs RGBI raster bands; Name of input imagery group
ndsm=name: Name of the nDSM raster; Name of input raster map
ndsm_scaled=name: Name of the scaled nDSM raster; Name of input raster map
reference=name: Name of the reference vector map; Or data source for direct OGR access
segmentation_minsize=integer: Minimum number of cells in a segment; Default: 80
segmentation_threshold=float: Difference threshold between 0 and 1 for the segments; Threshold = 0 merges only identical segments; threshold = 1 merges all; Default: 0.3
imagery_export_type=string [required]: Export type for imagery (default: Byte); Options: Byte, Int16, UInt16, Int32, UInt32, Float32, Float64, CInt16, CInt32, CFloat32, CFloat64; Default: Byte
output_dir=name [required]: Directory where the prepared data should be stored; The directory will be split into train and apply
tile_name=name [required]: Unique Name of the tile
orig_mapset=string [required]: Name of original mapset