I2Classification

Landsat5_old

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	10	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

Landsat8

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	16	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

Landsat8_old

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	10	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

Landsat8_usgs

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’, ‘B8’, ‘B9’, ‘B10’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	16	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

Landsat8_usgs_infrared

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
enable_sensor_gapfilling	False	Enable or disable gapfilling for landsat 8 and 9 IR data	bool	False	enable_sensor_gapfilling
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B10’, ‘B11’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	16	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

Landsat8_usgs_optical

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
enable_sensor_gapfilling	True	Enable or disable gapfilling for landsat 8 and 9 optical data	bool	False	enable_sensor_gapfilling
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	16	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

Landsat8_usgs_thermal

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
enable_sensor_gapfilling	False	Enable or disable gapfilling for landsat 8 and 9 thermal data(temperature and emissivity)	bool	False	enable_sensor_gapfilling
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B10’, ‘EMIS’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	16	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

Sentinel_2

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	10	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

Sentinel_2_L3A

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	10	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

Sentinel_2_S2C

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
end_date		The last date of interpolated image time series : YYYYMMDD format	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series : YYYYMMDD format	str	False	start_date
temporal_resolution	10	The temporal gap between two interpolations	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	flag to write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

Notes

end_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

keep_bands 

WARNING

For this parameter to be taken into account,the extract_bands variable in the iota2_feature_extraction section must also be set to True:

iota2_feature_extraction :
{
  'extract_bands':True,
}

start_date 

WARNING

For this parameter to be taken into account,the auto_date variable in the sensors_data_interpolationsection must also be set to False:

sensors_data_interpolation :
{
  'auto_date':False,
}

arg_classification

Name	Default Value	Description	Type	Mandatory	Name
boundary_comparison_mode	False	Enable classification comparison	bool	False	boundary_comparison_mode
boundary_exterior_buffer_size	0	Buffer size outside the region	int	False	boundary_exterior_buffer_size
boundary_fusion_epsilon	0.0	Threshold to avoid weights equals to zero	float	False	boundary_fusion_epsilon
boundary_interior_buffer_size	0	Buffer size inside the region	int	False	boundary_interior_buffer_size
classif_mode	separate	‘separate’ or ‘fusion’	str	False	classif_mode
dempstershafer_mob	precision	Choose the dempster shafer mass of belief estimation method	str	False	dempstershafer_mob
enable_boundary_fusion	False	Enable the boundary fusion	bool	False	enable_boundary_fusion
enable_probability_map	False	Produce the probability map	bool	False	enable_probability_map
fusion_options	-nodatalabel 0 -method majorityvoting	OTB FusionOfClassification options for voting method involved if classif_mode is set to ‘fusion’	str	False	fusion_options
fusionofclassification_all_samples_validation	False	Enable the use of all reference data to validate the classification merge	bool	False	fusionofclassification_all_samples_validation
generate_final_probability_map	False	Enable the mosaicing of probabilities maps.	bool	False	generate_final_probability_map
keep_runs_results	True		bool	False	keep_runs_results
merge_final_classifications	False	Enable the fusion of classifications mode, merging all run in a unique result.	bool	False	merge_final_classifications
merge_final_classifications_method	majorityvoting	Indicate the fusion of classification method: ‘majorityvoting’ or ‘dempstershafer’	str	False	merge_final_classifications_method
merge_final_classifications_ratio	0.1	Percentage of samples to use in order to evaluate the fusion raster	float	False	merge_final_classifications_ratio
merge_final_classifications_undecidedlabel	255	Indicate the label for undecision case during fusion	int	False	merge_final_classifications_undecidedlabel
no_label_management	maxConfidence	Method for choosing a label in case of fusion	str	False	no_label_management

Notes

boundary_comparison_mode 

If enabled it will produce two maps The boundary regions will be analysed to measure the method improvements.

boundary_exterior_buffer_size 

This value can be different than the interior buffer. The unit is in meter. The value is divided by the resolution to estimate the distance in pixels.

boundary_fusion_epsilon 

If the region shape contains the buffer operations are not bijective. In this condition, several weights can be set to 0. This lead to 0 area in the final map (holes).The weights maps are stored as uint16. The thresold should be higher enough to be different to zero once multiplied by 1000

boundary_interior_buffer_size 

This value can be different than the exterior buffer. The unit is in meter. The value is divided by the resolution to estimate the distance in pixels.

classif_mode 

If ‘fusion’ : too huge models will be devided into smaller ones and will classify the same pixels. The treshold between small/big models is define by the parameter ‘mode_outside_regionsplit’

dempstershafer_mob 

Two kind of indexes can be used: * Global: accuracy or kappa * Per class: precision or recall

enable_boundary_fusion 

If enabled probabilities are used to fuse maps between regions

enable_probability_map 

A probability map is a image with N bands , where N is the number of classes in the nomenclature file. The bands are sorted in ascending order more information more information here

fusionofclassification_all_samples_validation 

If the fusion mode is enabled, enable the use of all reference data samples for validation

generate_final_probability_map 

This operation can produce heavy maps.If you not need it over the whole area in one image, disable this option.

keep_runs_results 

If in fusion mode, two final reports can be provided. One for each seed, and one for the classification fusion

arg_train

Name	Default Value	Description	Type	Mandatory	Name
classifier	None	otb classification algorithm	str	False	classifier
deep_learning_parameters	{}	deep learning parameter description is available here	dict	False	deep_learning_parameters
features	[‘NDVI’, ‘NDWI’, ‘Brightness’]	List of additional features computed	list	False	features
features_from_raw_dates	False	learn model from raw sensor’s date (no interpolations)	bool	False	features_from_raw_dates
force_standard_labels	False	Standardize labels for feature extraction	bool	False	force_standard_labels
learning_samples_extension	sqlite	learning samples file extension, possible values are ‘sqlite’ and ‘csv’	str	False	learning_samples_extension
mode_outside_regionsplit	0.1	Set the threshold for split huge model	float	False	mode_outside_regionsplit
otb_classifier_options	None	OTB option for classifier.If None, the OTB default values are used	dict	False	otb_classifier_options
random_seed	None	Fix the random seed for random split of reference data	int	False	random_seed
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	False	ratio
runs	1	Number of independent runs processed	int	False	runs
sample_augmentation	{‘activate’: False, ‘bins’: 10}	OTB parameters for sample augmentation	dict	False	sample_augmentation
sample_management	None	Absolute path to a CSV file containing samples transfert strategies	str	False	sample_management
sample_selection	{‘sampler’: ‘random’, ‘strategy’: ‘all’}	OTB parameters for sampling the validation set	dict	False	sample_selection
sample_validation	{‘sampler’: ‘random’, ‘strategy’: ‘all’}	OTB parameters for sampling the validation set	dict	False	sample_validation
sampling_validation	False	Enable sampling validation	bool	False	sampling_validation
split_ground_truth	True	Enable the split of reference data	bool	False	split_ground_truth
validity_threshold	1	threshold above which a training pixel is considered valid	int	False	validity_threshold

Notes

features 

This parameter enable the computation of the three indices if available for the sensor used.There is no choice for using only one of them

features_from_raw_dates 

If True, during the learning and classification step, each pixel will receive a vector of values of the size of the number of all dates detected. As the pixels were not all acquired on the same dates, the vector will contains NaNs on the unacquired dates.

force_standard_labels 

The chain label each features by the sensors name, the spectral band or indice and the date. If activated this parameter use the OTB default value (value_X)

learning_samples_extension 

Default value is ‘sqlite’ (faster), in case of the number of features is superior to 2000, as sqlite file doesn’t accept more than 2000 columns, it should be turn to ‘csv’.

mode_outside_regionsplit 

This parameter is available if regionPath is used and arg_train.classif_mode is set to fusion. It represents the maximum size covered by a region. If the regions are larger than this threshold, then N models are built by randomly selecting features inside the region.

otb_classifier_options 

This parameter is a dictionnary which accepts all OTB application parameters. To know the exhaustive parameter list use otbcli_TrainVectorClassifier in a terminal or look at the OTB documentation

random_seed 

Fix the random seed used for random split of reference data If set, the results must be the same for a given classifier

runs 

Number of independent runs processed. Each run has his own learning samples. Must be an integer greater than 0

sample_augmentation 

In supervised classification the balance between class samples is important. There are any ways to manage class balancing in iota2, using refSampleSelection or the classifier’s options to limit the number of samples by class. An other approch is to generate synthetic samples. It is the purpose of this functionality, which is called ‘sample augmentation’.

{'activate':False}

Example

sample_augmentation : {'target_models':['1', '2'],
                      'strategy' : 'jitter',
                      'strategy.jitter.stdfactor' : 10,
                      'strategy.smote.neighbors'  : 5,
                      'samples.strategy' : 'balance',
                      'activate' : True
                      }

iota2 implements an interface to the OTB SampleAugmentation application. There are three methods to generate samples : replicate, jitter and smote.The documentation here explains the difference between these approaches.

samples.strategy specifies how many samples must be created.There are 3 different strategies: - minNumber

To set the minimum number of samples by class required

balance
balance all classes with the same number of samples as the majority one

byClass
augment only some of the classes

Parameters related to minNumber and byClass strategies are

samples.strategy.minNumber
minimum number of samples
samples.strategy.byClass
path to a CSV file containing in first column the class’s label and in the second column the minimum number of samples required.

In the above example, classes of models ‘1’ and ‘2’ will be augmented to the most represented class in the corresponding model using the jitter method.

To perform sample augmentation for regression problems, we return to a configuration similar to classification. For that purpose we create fake classes using the bins parameter. The bins parameter can be an integer in which case the interval of values of the target output variable is divided into bins sub-intervals of equal width, and each sample gets a fake a class corresponding to the number of the interval in which its label fell. bins can also be a list of ascending values used as interval boundaries for assign the classes.

sample_management 

The CSV must contain a row per transfert

>>> cat /absolute/path/myRules.csv
    1,2,4,2

Meaning :

source	destination	class name	quantity
1	2	4	2

Currently, setting the ‘random_seed’ parameter has no effect on this workflow.

sample_selection 

This field parameters the strategy of polygon sampling. It directly refers to options of OTB’s SampleSelection application.

Example

sample_selection : {'sampler':'random',
                   'strategy':'percent',
                   'strategy.percent.p':0.2,
                   'per_models':[{'target_model':'4',
                                  'sampler':'periodic'}]
                   }

In the example above, all polygons will be sampled with the 20% ratio. But the polygons which belong to the model 4 will be periodically sampled, instead of the ransom sampling used for other polygons. Notice than per_models key contains a list of strategies. Then we can imagine the following :

sample_selection : {'sampler':'random',
                   'strategy':'percent',
                   'strategy.percent.p':0.2,
                   'per_models':[{'target_model':'4',
                                  'sampler':'periodic'},
                                 {'target_model':'1',
                                  'sampler':'random',
                                  'strategy', 'byclass',
                                  'strategy.byclass.in', '/path/to/myCSV.csv'
                                 }]
                   }

where the first column of /path/to/myCSV.csv is class label (integer), second one is the required samples number (integer).

sample_validation 

This field parameters the strategy of polygon sampling. It directly refers to options of OTB’s SampleSelection application.

Example

sample_selection : {'sampler':'random',
                   'strategy':'percent',
                   'strategy.percent.p':0.2,
                   'per_models':[{'target_model':'4',
                                  'sampler':'periodic'}]
                   }

In the example above, all polygons will be sampled with the 20% ratio. But the polygons which belong to the model 4 will be periodically sampled, instead of the ransom sampling used for other polygons. Notice than per_models key contains a list of strategies. Then we can imagine the following :

sample_selection : {'sampler':'random',
                   'strategy':'percent',
                   'strategy.percent.p':0.2,
                   'per_models':[{'target_model':'4',
                                  'sampler':'periodic'},
                                 {'target_model':'1',
                                  'sampler':'random',
                                  'strategy', 'byclass',
                                  'strategy.byclass.in', '/path/to/myCSV.csv'
                                 }]
                   }

where the first column of /path/to/myCSV.csv is class label (integer), second one is the required samples number (integer).

split_ground_truth 

If set to False, the chain use all polygons for both training and validation

builders

Name	Default Value	Description	Type	Mandatory	Name
builders_class_name	[‘I2Classification’]	The name of the class defining the builder	list	False	builders_class_name
builders_paths	/path/to/iota2/sources	The path to user builders	str	False	builders_paths

Notes

builders_class_name 

Available builders are : ‘I2Classification’, ‘I2FeaturesMap’ and ‘I2Obia’

builders_paths 

If not indicated, the iota2 source directory is used: */iota2/sequence_builders/

chain

Name	Default Value	Description	Type	Mandatory	Name
check_inputs	True	Enable the inputs verification	bool	False	check_inputs
cloud_threshold	0	Threshold to consider that a pixel is valid	int	False	cloud_threshold
color_table	None	Absolute path to the file that links the classes and their colours	str	True	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	False	compression_algorithm
compression_predictor	2	Set the predictor for LZW and ZSTD compression: 1 (no predictor), 2 (horizontal differencing, default)	int	False	compression_predictor
data_field	None	Field name indicating classes labels in ground_thruth	str	True	data_field
first_step	None	The step group name indicating where the chain start	str	False	first_step
ground_truth	None	Absolute path to reference data	str	True	ground_truth
l5_path_old	None	Absolute path to Landsat-5 images coming from old THEIA format (DH)	str	False	l5_path_old
l8_path	None	Absolute path to Landsat-8 images comingfrom new tiled THEIA data	str	False	l8_path
l8_path_old	None	Absolute path to Landsat-8 images coming from old THEIA format (DH)	str	False	l8_path_old
l8_usgs_infrared_path	None	Absolute path to Landsat-8 images coming from USGS data	str	False	l8_usgs_infrared_path
l8_usgs_optical_path	None	Absolute path to Landsat-8 images coming from USGS data	str	False	l8_usgs_optical_path
l8_usgs_path	None	Absolute path to Landsat-8 images coming from USGS data	str	False	l8_usgs_path
l8_usgs_thermal_path	None	Absolute path to Landsat-8 images coming from USGS data	str	False	l8_usgs_thermal_path
last_step	None	The step group name indicating where the chain ends	str	False	last_step
list_tile	None	List of tile to process, separated by space	str	True	list_tile
logger_level	INFO	Set the logger level: NOTSET, DEBUG, INFO, WARNING, ERROR, CRITICAL	str	False	logger_level
minimum_required_dates	2	required minimum number of available dates for each sensor	int	False	minimum_required_dates
nomenclature_path	None	Absolute path to the nomenclature description file	str	True	nomenclature_path
output_path	None	Absolute path to the output directory	str	True	output_path
output_statistics	True	output_statistics	bool	False	output_statistics
proj	None	The projection wanted. Format EPSG:XXXX is mandatory	str	True	proj
region_field	region	The column name for region indicator in`region_path` file	str	False	region_field
region_path	None	Absolute path to a region vector file	str	False	region_path
remove_output_path	True	Before the launch of iota2, remove the content of output_path	bool	False	remove_output_path
s1_path	None	Absolute path to Sentinel-1 configuration file	str	False	s1_path
s2_l3a_output_path	None	Absolute path to store preprocessed data in a dedicated directory	str	False	s2_l3a_output_path
s2_l3a_path	None	Absolute path to Sentinel-2 L3A images (THEIA format)	str	False	s2_l3a_path
s2_output_path	None	Absolute path to store preprocessed data in a dedicated directory	str	False	s2_output_path
s2_path	None	Absolute path to Sentinel-2 images (THEIA format)	str	False	s2_path
s2_s2c_output_path	None	Absolute path to store preprocessed data in a dedicated directory	str	False	s2_s2c_output_path
s2_s2c_path	None	Absolute path to Sentinel-2 images (Sen2Cor format)	str	False	s2_s2c_path
spatial_resolution	[]	Output spatial resolution	list or scalar	False	spatial_resolution
user_feat_path	None	Absolute path to the user’s features path	str	False	user_feat_path

Notes

check_inputs 

Enable the inputs verification. It can take a lot of time for large dataset. Check if region intersect reference data for instance

cloud_threshold 

Indicates the threshold for a polygon to be used for learning. It use the validity count, which is incremented if a cloud, a cloud shadow or a saturated pixel is detected

compression_predictor 

It has been noted that in some cases, once the features are written to disk, the raster file may be empty. If this is the case, please change the predictor to 1 or 3.

data_field 

All the labels values must be different to 0. It is recommended to use a continuous range of values but it is not mandatory. Keep in mind that the final product type is detected according to the maximum label value. Try to keep values between 1 and 255 to avoid heavy products.

output_path 

Absolute path to the output directory.It is recommended to have one directory per run of the chain

region_field 

This column in the database must contains string which can be converted into integers. For instance ‘1_2’ does not match this condition. It is mandatory that the region identifiers are > 0.

remove_output_path 

Before the launch of iota2, remove the content of output_path. Only if the first_step is init and the folder name is valid

spatial_resolution 

The spatial resolution expected.It can be provided as integer or float,or as a list containing two values for non squared resolution

user_feat_path 

Absolute path to the user’s features path. They must be stored by tiles

external_features

Name	Default Value	Description	Type	Mandatory	Name
concat_mode	True	enable the use of all features	bool	False	concat_mode
exogeneous_data	None	Path to a Geotiff file containing additional data to be used in external features	str	False	exogeneous_data
external_features_flag	False	enable the external features mode	bool	False	external_features_flag
functions	None	function list to be used to compute features	str/list	False	functions
module	/path/to/iota2/sources	absolute path for user source code	str	False	module
no_data_value	-10000	value considered as no_data in features map mosaic (‘I2FeaturesMap’ builder name)	int	False	no_data_value
output_name	None	temporary chunks are written using this name as prefix	str	False	output_name

Notes

concat_mode 

if disabled, only external features are used in the whole processing

exogeneous_data 

If the =exogeneous_data= contains ‘$TILE’, it will be replaced by the tile name being processed.If you want to reproject your data on given tiles, you can use the =split_raster_into_tiles.py= command line tool.

Usage: =split_raster_into_tiles.py –help=.

functions 

Can be a string of space-separated function namesCan be a list of either strings of function nameor lists of one function name and one argument mapping

iota2_feature_extraction

Name	Default Value	Description	Type	Mandatory	Name
acor_feat	False	Apply atmospherically corrected features	bool	False	acor_feat
copy_input	True	use spectral bands as features	bool	False	copy_input
extract_bands	False		bool	False	extract_bands
keep_duplicates	True	use ‘rel_refl’ can generate duplicated feature (ie: NDVI), set to False remove these duplicated features	bool	False	keep_duplicates
rel_refl	False	compute relative reflectances by the red band	bool	False	rel_refl

Notes

acor_feat 

Apply atmospherically corrected featuresas explained at : http://www.cesbio.ups-tlse.fr/multitemp/?p=12746

pretrained_model

Name	Default Value	Description	Type	Mandatory	Name
boundary_buffer	None	List of boundary buffer size	list	False	boundary_buffer
function	None	Predict function name	str	False	function
mode	None	Algorythm nature (classification or regression)	str	False	mode
model	None	Serialized object containing the model	str	False	model
module	/path/to/iota2/sources	Absolute path to the python module	str	False	module

Notes

function 

This function must have the imposed signature. It not accept any others parameters. All model dedicated parameters must be stored alongside the model.

mode 

The python module must contains the predict function It must handle all the potential dependencies and import related to the correct model instanciation

model 

In the configuration file, the mandatory keys $REGION and $SEED must be present as they are replaced by iota2. In case of only one region, the region value is set to 1. Look at the documentation about the model constraint.

module 

The python module must contains the predict function It must handle all the potential dependencies and import related to the correct model instanciation

python_data_managing

Name	Default Value	Description	Type	Mandatory	Name
chunk_size_mode	split_number	The chunk split mode, currently the choice is ‘split_number’	str	False	chunk_size_mode
chunk_size_x	50	number of cols for one chunk	int	False	chunk_size_x
chunk_size_y	50	number of rows for one chunk	int	False	chunk_size_y
data_mode_access	gapfilled	choose which data can be accessed in custom features	str	False	data_mode_access
fill_missing_dates	False	fill raw data with no data if dates are missing	bool	False	fill_missing_dates
max_nn_inference_size	None	maximum batch inference size	int	False	max_nn_inference_size
number_of_chunks	50	the expected number of chunks	int	False	number_of_chunks
padding_size_x	0	The padding for chunk	int	False	padding_size_x
padding_size_y	0	The padding for chunk	int	False	padding_size_y

Notes

data_mode_access 

Three values are allowed:

gapfilled: give access only the gapfilled data
raw: gives access only the original raw data
both: provides access to both data

..Notes:: Data are spatialy resampled, these parameters concern only temporal interpolation

fill_missing_dates 

If raw data access is enabled, this option considers all unique dates for all tiles and identify which dates are missing for each tile. A missing date is filled using a no data constant value.Cloud or saturation are not corrected, but masks are provided Masks contain three value: 0 for valid data, 1 for cloudy or saturated pixels, 2 for a missing date

max_nn_inference_size 

Involved if a neural network inference is performed. If not set (None), the inference size will be the same as the one used during the learning stage

scikit_models_parameters

Name	Default Value	Description	Type	Mandatory	Name
cross_validation_folds	5	the number of k-folds	int	False	cross_validation_folds
cross_validation_grouped	False		bool	False	cross_validation_grouped
cross_validation_parameters	{}		dict	False	cross_validation_parameters
keyword_arguments	{}	keyword arguments to be passed to model	dict	False	keyword_arguments
model_type	None	machine learning algorthm’s name	str	False	model_type
standardization	True		bool	False	standardization

Notes

keyword_arguments 

keyword arguments to be passed to model

model_type 

Models comming from scikit-learn are use if scikit_models_parameters.model_type is different from None. More informations about how to use scikit-learn is available at iota2 and scikit-learn machine learning algorithms.

sensors_data_interpolation

Name	Default Value	Description	Type	Mandatory	Name
auto_date	True	Enable the use of start_date and end_date	bool	False	auto_date
use_additional_features	False	enable the use of additional features	bool	False	use_additional_features
use_gapfilling	True	enable the use of gapfilling (clouds/temporal interpolation)	bool	False	use_gapfilling
write_outputs	False	write temporary files	bool	False	write_outputs

Notes

auto_date 

If True, iota2 will automatically guess the first and the last interpolation date. Else, start_date and end_date of each sensors will be used

write_outputs 

Write the time series before and after gapfilling, the mask time series, and also the feature time series. This option required a large amount of free disk space.

slurm

Name	Default Value	Description	Type	Mandatory	Name
account	None	Feed the sbatch parameter ‘account’	str	False	account

Notes

account 

The section ‘slurm’ is only available once the Slurm orchestrator is involved in jobs submission.

task_retry_limits

Name	Default Value	Description	Type	Mandatory	Name
allowed_retry	0	allow dask to retry a failed job N times	int	False	allowed_retry
maximum_cpu	4	the maximum number of CPU available	int	False	maximum_cpu
maximum_ram	16.0	the maximum amount of RAM available. (gB)	float	False	maximum_ram

Notes

maximum_cpu 

the amount of cpu will be doubled if the task is killed due to ram overconsumption until maximum_cpu or allowed_retry are reach

maximum_ram 

the amount of RAM will be doubled if the task is killed due to ram overconsumption until maximum_ram or allowed_retry are reach

userFeat

Name	Default Value	Description	Type	Mandatory	Name
arbo	/*	input folder hierarchy	str	False	arbo
patterns	ALT,ASP,SLP	key name for detect the input images	str	False	patterns

Other Versions v: develop

Branches: develop; master