i2_obia

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 end date of interpolated image time series	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series	str	False	start_date
temporal_resolution	16	The temporal gap between two interpolation	int	False	temporal_resolution

Landsat8

Name	Default Value	Description	Type	Mandatory	Name
additional_features		OTB’s bandmath expressions, separated by comma	str	False	additional_features
end_date		The end date of interpolated image time series	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	str	False	start_date
temporal_resolution	16	The temporal gap between two interpolation	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	Enable the write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

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 end date of interpolated image time series	str	False	end_date
keep_bands	[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series	str	False	start_date
temporal_resolution	16	The temporal gap between two interpolation	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	Enable the write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

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 end date of interpolated image time series	str	False	end_date
keep_bands	[‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’, ‘B8’, ‘B8A’, ‘B11’, ‘B12’]	The list of spectral bands used for classification	list	False	keep_bands
temporal_resolution	10	The temporal gap between two interpolation	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	Enable the write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

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 end date of interpolated image time series	str	False	end_date
keep_bands	[‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’, ‘B8’, ‘B8A’, ‘B11’, ‘B12’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series	str	False	start_date
temporal_resolution	16	The temporal gap between two interpolation	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	Enable the write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

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 end date of interpolated image time series	str	False	end_date
keep_bands	[‘B02’, ‘B03’, ‘B04’, ‘B05’, ‘B06’, ‘B07’, ‘B08’, ‘B8A’, ‘B11’, ‘B12’]	The list of spectral bands used for classification	list	False	keep_bands
start_date		The first date of interpolated image time series	str	False	start_date
temporal_resolution	10	The temporal gap between two interpolation	int	False	temporal_resolution
write_reproject_resampled_input_dates_stack	True	Enable the write of resampled stack image for each date	bool	False	write_reproject_resampled_input_dates_stack

arg_train

Name	Default Value	Description	Type	Mandatory	Name
classifier	None	Choose the classification algorithm	str	False	classifier
dempster_shafer_sar_opt_fusion	False	Enable the use of both SAR and optical data to train a model.	bool	False	dempster_shafer_sar_opt_fusion
mode_outside_regionsplit	0.1	Fix 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 independant runs processed.	int	False	runs
sample_augmentation	{‘activate’: False}	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 sample selection	dict	False	sample_selection
split_ground_truth	True	Enable the split of reference data	bool	False	split_ground_truth
validity_threshold	1		int	False	validity_threshold

Notes

dempster_shafer_sar_opt_fusion

Enable the use of both SAR and optical data to train a model. If True then two models are trained.more documentation is avalailalbe here

mode_outside_regionsplit

This parameter is available if regionPath is used and arg_classification.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 independant 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 sample_selection 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 thisfunctionality, 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 thethe most represented class in the corresponding model using the jitter method.

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

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).

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	[‘i2_classification’]	The name of the class defining the builder	list	False	builders_class_name
builders_paths	/path/to/iota2/sources	The path to user builders	list	False	builders_paths

Notes

builders_class_name

Available builders are : ‘i2_classification’, ‘i2_features_map’, ‘i2_obia’ and ‘i2_vectorization’

builders_paths

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

chain

Name	Default Value	Description	Type	Mandatory	Name
cloud_threshold	0	Threshold to consider that a pixel is valid	int	False	cloud_threshold
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	True	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
last_step	None	The step group name indicating where the chain ends	str	True	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
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	EPSG:2154	The projection wanted. Format EPSG:XXXX is mandatory	str	False	proj
region_field	region	The column name for region indicator in`region_path` file	str	False	region_field
region_path	None	Absolute path to region vector file	str	False	region_path
remove_output_path	True	Enable the removing of complete output_path directory	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	10	Output spatial resolution	list	False	spatial_resolution
user_feat_path	None	Absolute path to the user’s features path	str	False	user_feat_path

Notes

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

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

remove_output_path

Enable the removing of complete output_path directory 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	True	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 a 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.

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	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.

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	The maximum amount of RAM available. (gB)	int	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	/*	The input folder hierarchy	str	False	arbo
patterns	ALT,ASP,SLP	key name for detect the input images	str	False	patterns