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All configuration parameters

Name	Default Value	Description	Type	Name
account	None	Feed the sbatch parameter ‘account’	str	account
acor_feat	False	Apply atmospherically corrected features	bool	acor_feat
additional_features		OTB’s bandmath expressions, separated by comma	str	additional_features
allowed_retry	0	allow dask to retry a failed job N times	int	allowed_retry
arbo	/*	input folder hierarchy	str	arbo
auto_date	True	Enable the use of start_date and end_date	bool	auto_date
boundary_buffer	None	List of boundary buffer size	list	boundary_buffer
boundary_comparison_mode	False	Enable classification comparison	bool	boundary_comparison_mode
boundary_exterior_buffer_size	0	Buffer size outside the region	int	boundary_exterior_buffer_size
boundary_fusion_epsilon	0.0	Threshold to avoid weights equals to zero	float	boundary_fusion_epsilon
boundary_interior_buffer_size	0	Buffer size inside the region	int	boundary_interior_buffer_size
buffer_size	None	define the working size batch in number of pixels	int	buffer_size
builders_class_name	[‘I2Classification’]	The name of the class defining the builder	list	builders_class_name
builders_paths	/path/to/iota2/sources	The path to user builders	str	builders_paths
check_inputs	True	Enable the inputs verification	bool	check_inputs
chunk_size_mode	split_number	The chunk split mode, currently the choice is ‘split_number’	str	chunk_size_mode
chunk_size_x	50	number of cols for one chunk	int	chunk_size_x
chunk_size_y	50	number of rows for one chunk	int	chunk_size_y
classif_mode	separate	‘separate’ or ‘fusion’	str	classif_mode
classifier	None	otb classification algorithm	str	classifier
cloud_threshold	0	Threshold to consider that a pixel is valid	int	cloud_threshold
color_table	None	Absolute path to the file that links the classes and their colours	str	color_table
compression_algorithm	ZSTD	Set the gdal compression algorithm to use: NONE, LZW, ZSTD (default).All rasters write with OTB will be compress with the chosen algorithm.	str	compression_algorithm
compression_predictor	2	Set the predictor for LZW and ZSTD compression: 1 (no predictor), 2 (horizontal differencing, default)	int	compression_predictor
concat_mode	True	enable the use of all features	bool	concat_mode
copy_input	True	use spectral bands as features	bool	copy_input
cross_validation_folds	5	the number of k-folds	int	cross_validation_folds
cross_validation_grouped	False		bool	cross_validation_grouped
cross_validation_parameters	{}		dict	cross_validation_parameters
data_field	None	Field name indicating classes labels in ground_thruth	str	data_field
data_mode_access	gapfilled	choose which data can be accessed in custom features	str	data_mode_access
deep_learning_parameters	{}	deep learning parameter description is available here	dict	deep_learning_parameters
dempstershafer_mob	precision	Choose the dempster shafer mass of belief estimation method	str	dempstershafer_mob
enable_boundary_fusion	False	Enable the boundary fusion	bool	enable_boundary_fusion
enable_probability_map	False	Produce the probability map	bool	enable_probability_map
enable_sensor_gapfilling	False	Enable or disable gapfilling for landsat 8 and 9 IR data	bool	enable_sensor_gapfilling
end_date		The last date of interpolated image time series : YYYYMMDD format	str	end_date
exogeneous_data	None	Path to a Geotiff file containing additional data to be used in external features	str	exogeneous_data
external_features_flag	False	enable the external features mode	bool	external_features_flag
extract_bands	False		bool	extract_bands
features	[‘NDVI’, ‘NDWI’, ‘Brightness’]	List of additional features computed	list	features
features_from_raw_dates	False	learn model from raw sensor’s date (no interpolations)	bool	features_from_raw_dates
features_path	None	input directory containing features as rasters	str	features_path
fill_missing_dates	False	fill raw data with no data if dates are missing	bool	fill_missing_dates
first_step	None	The step group name indicating where the chain start	str	first_step
force_standard_labels	False	Standardize labels for feature extraction	bool	force_standard_labels
from_rasterdb_resampling_method	nn	output features type choice among gdalwarp.html#cmdoption-gdalwarp-r. Enabled if chain.rasters_grid_path is set	str	from_rasterdb_resampling_method
from_vectordb_resampling_method	near	output features type choice among gdalwarp.html#cmdoption-gdalwarp-r. Enabled if chain.grid is set	str	from_vectordb_resampling_method
full_learn_segment	False	enable the use of entire segment for learning	bool	full_learn_segment
function	None	Predict function name	str	function
functions	None	function list to be used to compute features	str/list	functions
fusion_options	-nodatalabel 0 -method majorityvoting	OTB FusionOfClassification options for voting method involved if classif_mode is set to ‘fusion’	str	fusion_options
fusionof_all_samples_validation	False	Enable the use of all reference data to evaluate the fusion raster	bool	fusionof_all_samples_validation
fusionofclassification_all_samples_validation	False	Enable the use of all reference data to validate the classification merge	bool	fusionofclassification_all_samples_validation
generate_final_probability_map	False	Enable the mosaicing of probabilities maps.	bool	generate_final_probability_map
grid	None	input grid to fit	str	grid
gridsize	None	number of lines and columns of the serialization process	int	gridsize
ground_truth	None	Absolute path to reference data	str	ground_truth
inland	None	inland water limit shapefile	str	inland
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’]	The list of spectral bands used for classification	list	keep_bands
keep_duplicates	True	use ‘rel_refl’ can generate duplicated feature (ie: NDVI), set to False remove these duplicated features	bool	keep_duplicates
keep_runs_results	True		bool	keep_runs_results
keyword_arguments	{}	keyword arguments to be passed to model	dict	keyword_arguments
l5_path_old	None	Absolute path to Landsat-5 images coming from old THEIA format (D*H*)	str	l5_path_old
l8_path	None	Absolute path to Landsat-8 images comingfrom new tiled THEIA data	str	l8_path
l8_path_old	None	Absolute path to Landsat-8 images coming from old THEIA format (D*H*)	str	l8_path_old
l8_usgs_infrared_path	None	Absolute path to Landsat-8 images coming from USGS data	str	l8_usgs_infrared_path
l8_usgs_optical_path	None	Absolute path to Landsat-8 images coming from USGS data	str	l8_usgs_optical_path
l8_usgs_path	None	Absolute path to Landsat-8 images coming from USGS data	str	l8_usgs_path
l8_usgs_thermal_path	None	Absolute path to Landsat-8 images coming from USGS data	str	l8_usgs_thermal_path
last_step	None	The step group name indicating where the chain ends	str	last_step
learning_samples_extension	sqlite	learning samples file extension, possible values are ‘sqlite’ and ‘csv’	str	learning_samples_extension
list_tile	None	List of tile to process, separated by space	str	list_tile
logger_level	INFO	Set the logger level: NOTSET, DEBUG, INFO, WARNING, ERROR, CRITICAL	str	logger_level
max_nn_inference_size	None	maximum batch inference size	int	max_nn_inference_size
maximum_cpu	4	the maximum number of CPU available	int	maximum_cpu
maximum_ram	16.0	the maximum amount of RAM available. (gB)	float	maximum_ram
merge_final_classifications	False	Enable the fusion of classifications mode, merging all run in a unique result.	bool	merge_final_classifications
merge_final_classifications_method	majorityvoting	Indicate the fusion of classification method: ‘majorityvoting’ or ‘dempstershafer’	str	merge_final_classifications_method
merge_final_classifications_ratio	0.1	Percentage of samples to use in order to evaluate the fusion raster	float	merge_final_classifications_ratio
merge_final_classifications_undecidedlabel	255	Indicate the label for undecision case during fusion	int	merge_final_classifications_undecidedlabel
merge_run	False	Enable the fusion of regression mode, merging all run in a unique result.	bool	merge_run
merge_run_method	mean	Indicate the fusion of regression method: ‘mean’ or ‘median’	str	merge_run_method
merge_run_ratio	0.1	Percentage of samples to use in order to evaluate the fusion raster	float	merge_run_ratio
minimum_required_dates	2	required minimum number of available dates for each sensor	int	minimum_required_dates
mode	None	Algorythm nature (classification or regression)	str	mode
mode_outside_regionsplit	0.1	Set the threshold for split huge model	float	mode_outside_regionsplit
model	None	Serialized object containing the model	str	model
model_type	None	machine learning algorthm’s name	str	model_type
module	/path/to/iota2/sources	absolute path for user source code	str	module
no_data_value	-10000	value considered as no_data in features map mosaic (‘I2FeaturesMap’ builder name)	int	no_data_value
no_label_management	maxConfidence	Method for choosing a label in case of fusion	str	no_label_management
nomenclature	None	configuration file which describe nomenclature	configuration file which describe nomenclature	nomenclature
nomenclature_path	None	Absolute path to the nomenclature description file	str	nomenclature_path
number_of_chunks	50	the expected number of chunks	int	number_of_chunks
obia_segmentation_path	None	filename for input segmentation	str	obia_segmentation_path
otb_classifier_options	None	OTB option for classifier.If None, the OTB default values are used	dict	otb_classifier_options
out_worldclim_dtype	float32	output worlclim data type, ie : ‘uint16’, ‘float32’.	np.dtype	out_worldclim_dtype
out_worldclim_rescale_range	None	rescale worldclim data between 0 and max(np.dtype) at run time for RAM usage purpose.	np.dtype	out_worldclim_rescale_range
output_features_pix_type	float	output features type choice among uint8/uint16/int16/uint32/int32/float/double.	str	output_features_pix_type
output_name	None	temporary chunks are written using this name as prefix	str	output_name
output_path	None	Absolute path to the output directory	str	output_path
output_statistics	True	output_statistics	bool	output_statistics
padding_size_x	0	The padding for chunk	int	padding_size_x
padding_size_y	0	The padding for chunk	int	padding_size_y
patterns	ALT,ASP,SLP	key name for detect the input images	str	patterns
proj	None	The projection wanted. Format EPSG:XXXX is mandatory	str	proj
random_seed	None	Fix the random seed for random split of reference data	int	random_seed
rasters_grid_path	None	input grid to fit	str	rasters_grid_path
ratio	0.5	Should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split.	float	ratio
region_field	region	The column name for region indicator in`region_path` file	str	region_field
region_path	None	Absolute path to a region vector file	str	region_path
region_priority	None	define an order for region intersection	list	region_priority
rel_refl	False	compute relative reflectances by the red band	bool	rel_refl
remove_output_path	True	Before the launch of iota2, remove the content of output_path	bool	remove_output_path
resampling_bco_radius	2	otb radius for bicubic interpolation.	int	resampling_bco_radius
rssize	20	Resampling size of input classification raster (projection unit)	int	rssize
runs	1	Number of independent runs processed	int	runs
s1_dir	None	Sentinel1 data directory	str	s1_dir
s1_path	None	Absolute path to Sentinel-1 configuration file	str	s1_path
s2_l3a_output_path	None	Absolute path to store preprocessed data in a dedicated directory	str	s2_l3a_output_path
s2_l3a_path	None	Absolute path to Sentinel-2 L3A images (THEIA format)	str	s2_l3a_path
s2_output_path	None	Absolute path to store preprocessed data in a dedicated directory	str	s2_output_path
s2_path	None	Absolute path to Sentinel-2 images (THEIA format)	str	s2_path
s2_s2c_output_path	None	Absolute path to store preprocessed data in a dedicated directory	str	s2_s2c_output_path
s2_s2c_path	None	Absolute path to Sentinel-2 images (Sen2Cor format)	str	s2_s2c_path
sample_augmentation	{‘activate’: False, ‘bins’: 10}	OTB parameters for sample augmentation	dict	sample_augmentation
sample_management	None	Absolute path to a CSV file containing samples transfert strategies	str	sample_management
sample_selection	{‘sampler’: ‘random’, ‘strategy’: ‘all’}	OTB parameters for sampling the validation set	dict	sample_selection
sample_validation	{‘sampler’: ‘random’, ‘strategy’: ‘all’}	OTB parameters for sampling the validation set	dict	sample_validation
sampling_validation	False	Enable sampling validation	bool	sampling_validation
spatial_resolution	[]	Output spatial resolution	list or scalar	spatial_resolution
split_ground_truth	True	Enable the split of reference data	bool	split_ground_truth
srtm_path	None	Path to a directory containing srtm data	str	srtm_path
standardization	True		bool	standardization
start_date		The first date of interpolated image time series : YYYYMMDD format	str	start_date
stats_used	[‘mean’]	list of stats used for train and classification	list	stats_used
temporal_resolution	10	The temporal gap between two interpolations	int	temporal_resolution
tile_field	None	column name in ‘grid’ containing tile’s name.	str	tile_field
umc1	None	MMU for the first regularization	int	umc1
umc2	None	MMU for the second regularization	int	umc2
use_additional_features	False	enable the use of additional features	bool	use_additional_features
use_gapfilling	True	enable the use of gapfilling (clouds/temporal interpolation)	bool	use_gapfilling
user_feat_path	None	Absolute path to the user’s features path	str	user_feat_path
validity_threshold	1	threshold above which a training pixel is considered valid	int	validity_threshold
worldclim_path	None	Path to a directory containing world clim data	str	worldclim_path
write_outputs	False	write temporary files	bool	write_outputs
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	write_reproject_resampled_input_dates_stack