You are looking the documentation of a development version. The release version is available at master.

i2_classification

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

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

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

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

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

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

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

a_crop_label_replacement

[‘10’, ‘annual_crop’]

Replace a label by a string, ie [‘10’, ‘annual_crop’]

list

False

a_crop_label_replacement

annual_classes_extraction_source

None

str

False

annual_classes_extraction_source

annual_crop

[‘11’, ‘12’]

The list of classes to be replaced by previous data

list

False

annual_crop

autocontext_iterations

3

Number of iterations in auto-context

int

False

autocontext_iterations

classifier

None

otb classification algorithm

str

False

classifier

crop_mix

False

Enable crop mix workflow

bool

False

crop_mix

deep_learning_parameters

{}

deep learning parameter description is available here

dict

False

deep_learning_parameters

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

enable_autocontext

False

Enable the auto-context processing

bool

False

enable_autocontext

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

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

output_prev_features

None

Path to previous features for crop mix

str

False

output_prev_features

prev_features

None

Path to a configuration file used to produce previous features

str

False

prev_features

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 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

samples_classif_mix

False

Enable the second step of crop mix

bool

False

samples_classif_mix

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

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

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)

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 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 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 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).

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

[‘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

str

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

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

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

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

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

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

coregistration

Name

Default Value

Description

Type

Mandatory

Name

band_ref

1

Date YYYYMMDD of the reference image

int

False

band_ref

band_src

3

Number of the band of the VHR image to use for coregistration

int

False

band_src

date_src

None

Date YYYYMMDD of the reference image

str

False

date_src

date_vhr

None

Date YYYYMMDD of the VHR image

str

False

date_vhr

iterate

True

Minimal number of SIFT points to find to create the new RPC model

bool

False

iterate

minsiftpoints

40

Minimal number of SIFT points to find to create the new RPC model

int

False

minsiftpoints

minstep

16

Minimal size of steps between bins in pixels

int

False

minstep

mode

2

Coregistration mode of the time series

int

False

mode

pattern

None

Pattern of the time series files to coregister

str

False

pattern

prec

3

Estimated shift between source and reference raster in pixel (source raster resolution)

int

False

prec

resample

True

Resample the reference and the source rasterto the same resolution to find SIFT points

bool

False

resample

step

256

Initial size of steps between bins in pixels

int

False

step

vhr_path

None

Absolute path to the VHR file

str

False

vhr_path

Notes

band_ref

If no date_src is mentionned, the best image will be automatically choose for coregistration

date_src

If no date_src is mentionned, the best image will be automatically choose for coregistration

mode

Mode

Method

1

single coregistration between one source image (and its masks) and the VHR image

2

this mode operates a coregistration between a image of the timeseries and the VHR image, then the same RPC model is used to orthorectify every images of the timeseries

3

cascade mode, this mode operates a first coregistration between a source image and the VHR image, then each image of the timeseries is coregistered step by step with the closest temporal images of the timeseries already coregistered

pattern

By default the value is left to None and the pattern depends on the sensor used (STACK.tif for Sentinel2, ORTHO_SURF_CORR_PENTE.TIF)

Examples
pattern: '*STACK.tif'

dim_red

Name

Default Value

Description

Type

Mandatory

Name

dim_red

False

Enable the dimensionality reduction mode

bool

False

dim_red

reduction_mode

global

The reduction mode

str

False

reduction_mode

target_dimension

4

The number of dimension required, according to reduction_mode

int

False

target_dimension

Notes

reduction_mode

Values authorized are: ‘global’ or ‘?’

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 (‘i2_features_map’ 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

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

model_type

None

machine learning algorthm’s name

str

False

model_type

standardization

False

bool

False

standardization

Notes

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.

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