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I2Obia

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_train

Name	Default Value	Description	Type	Mandatory	Name
classifier	None	otb classification algorithm	str	False	classifier
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_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
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

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_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 (D*H*)	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 (D*H*)	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
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

obia

Name	Default Value	Description	Type	Mandatory	Name
buffer_size	None	define the working size batch in number of pixels	int	False	buffer_size
full_learn_segment	False	enable the use of entire segment for learning	bool	False	full_learn_segment
obia_segmentation_path	None	filename for input segmentation	str	False	obia_segmentation_path
region_priority	None	define an order for region intersection	list	False	region_priority
stats_used	[‘mean’]	list of stats used for train and classification	list	False	stats_used

Notes

buffer_size

this parameter is used to avoid memory issue.In case of a large temporal series,i.e one year of Sentinel2 images a recommended size is 2000.For lower number of date, the buffer size can be increased.If buffer_size is larger than the image size, the whole image will be processed in one time.

full_learn_segment

if True: keep each segment which intersect the learning samples. If False, the segments are clipped with learning polygon shape

obia_segmentation_path

If parameter is None then a segmentation for each tile is processed using SLIC algorithm

region_priority

if a list is provided, the list order is used instead of the numeric order.This option can be used in case of very unbalanced region size.

stats_used

this list accepts only five values: mean, count, min, max, std The choice of statistics used should be considered in relation to the number of dates used.Because of the constraints on vector formats, one must think about the number of features this creates: nb_stats_choosen * nb_bands * nb_dates. Too many spectral bands can cause an error in the execution of the string.

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

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.

simplification

Name	Default Value	Description	Type	Mandatory	Name
gridsize	None	number of lines and columns of the serialization process	int	False	gridsize
inland	None	inland water limit shapefile	str	False	inland
nomenclature	None	configuration file which describe nomenclature	configuration file which describe nomenclature	False	nomenclature
rssize	20	Resampling size of input classification raster (projection unit)	int	False	rssize
umc1	None	MMU for the first regularization	int	False	umc1
umc2	None	MMU for the second regularization	int	False	umc2

Notes

gridsize

This parameter is useful only for large areas for which vectorization process can not be executed (memory limitation). By ‘serialization’, we mean parallel vectorization processes. If not None, regularized classification raster is splitted in gridsize x gridsize rasters

inland

to vectorize only inland waters, and not unnecessary sea water areas

nomenclature

This configuration file includes code, color, description and vector field alias of each class

Classes:
{
        Level1:
        {
                "Urbain":
                {
                code:100
                alias:"Urbain"
                color:"#b106b1"
                }
                ...
        }
        Level2:
        {
               "Urbain dense":
               {
               code:1
               alias:"UrbainDens"
               color:"#ff00ff"
               parent:100
               }
               ...
        }
}

rssize

OSO-like vectorization requires a resampling step in order to regularize and decrease raster polygons number, If None, classification is not resampled

umc1

It is an interface of parameter ‘-st’ of gdal_sieve.py function. If None, classification is not regularized

umc2

OSO-like vectorization process requires 2 successive regularization, if you need a single regularization, let this parameter to None

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