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 columns 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 written with OTB will be compressed 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_truth	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 starts.	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 app_Superimpose.html#parameters. 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	Functions 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	Using ‘rel_refl’ can generate duplicated features (ie: NDVI). Set to False remove these duplicated features.	bool	keep_duplicates
keep_runs_results	True	If in fusion mode, two final reports can be provided. One for each seed, and one for the classification fusion.	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 tiles 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_indecidedlabel	255	Indicate the label for indecision case during fusion.	int	merge_final_classifications_indecidedlabel
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_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	Algorithm nature (classification or regression).	str	mode
mode_outside_regionsplit	0.1	Set the threshold to split huge model.	float	mode_outside_regionsplit
model	None	Serialized object containing the model.	str	model
model_type	None	Machine learning algorithm’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 describes nomenclature.	Configuration file which describes 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 WorldClim 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 to 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 represents 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	Sentinel-1 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 transfer 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 WorldClim data.	str	worldclim_path
write_outputs	False	Write temporary files.	bool	write_outputs
write_reproject_resampled_input_dates_stack	True	Flag to write resampled stack image for each date.	bool	write_reproject_resampled_input_dates_stack