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All configuration parameters

Name

Default Value

Description

Type

Name

a_crop_label_replacement

[‘10’, ‘annual_crop’]

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

list

a_crop_label_replacement

account

None

Feed the sbatch parameter ‘account’

str

account

acor_feat

False

Apply atmospherically corrected features

bool

acor_feat

additional_features

OTB’s bandmath expressions, separated by comma

str

additional_features

allowed_retry

0

allow dask to retry a failed job N times

int

allowed_retry

angle

True

if True, smoothing corners of pixels (45°)

bool

angle

annual_classes_extraction_source

None

str

annual_classes_extraction_source

annual_crop

[‘11’, ‘12’]

The list of classes to be replaced by previous data

list

annual_crop

arbo

/*

input folder hierarchy

str

arbo

auto_date

True

Enable the use of start_date and end_date

bool

auto_date

autocontext_iterations

3

Number of iterations in auto-context

int

autocontext_iterations

band_ref

1

Date YYYYMMDD of the reference image

int

band_ref

band_src

3

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

int

band_src

bingdal

None

path to GDAL binaries

str

bingdal

blocksize

2000

block size to split raster to prevent Numpy memory error

str

blocksize

boundary_comparison_mode

False

Enable classification comparison

bool

boundary_comparison_mode

boundary_exterior_buffer_size

0

Buffer size outside the region

int

boundary_exterior_buffer_size

boundary_fusion_epsilon

0.0

Threshold to avoid weights equals to zero

float

boundary_fusion_epsilon

boundary_interior_buffer_size

0

Buffer size inside the region

int

boundary_interior_buffer_size

buffer_size

None

define the working size batch in number of pixels

int

buffer_size

builders_class_name

[‘i2_classification’]

The name of the class defining the builder

list

builders_class_name

builders_paths

/path/to/iota2/sources

The path to user builders

str

builders_paths

check_inputs

True

Enable the inputs verification

bool

check_inputs

chunk

10

number of chunks for statistics computing

int

chunk

chunk_size_mode

split_number

The chunk split mode, currently the choice is ‘split_number’

str

chunk_size_mode

chunk_size_x

50

number of cols for one chunk

int

chunk_size_x

chunk_size_y

50

number of rows for one chunk

int

chunk_size_y

classif_mode

separate

‘separate’ or ‘fusion’

str

classif_mode

classification

None

Input raster of classification

str

classification

classifier

None

otb classification algorithm

str

classifier

clipfield

None

field to identify distinct areas

str

clipfield

clipfile

None

vector-based file to clip output vector-based classification

str

clipfile

clipvalue

None

value of field which identify distinct areas

int

clipvalue

cloud_threshold

0

Threshold to consider that a pixel is valid

int

cloud_threshold

color_table

None

Absolute path to the file that links the classes and their colours

str

color_table

compression_algorithm

ZSTD

Set the gdal compression algorithm to use: NONE, LZW, ZSTD (default).All rasters write with OTB will be compress with the chosen algorithm.

str

compression_algorithm

compression_predictor

2

Set the predictor for LZW and ZSTD compression: 1 (no predictor), 2 (horizontal differencing, default)

int

compression_predictor

concat_mode

True

enable the use of all features

bool

concat_mode

confidence

None

Input raster of confidence

str

confidence

copy_input

True

use spectral bands as features

bool

copy_input

crop_mix

False

Enable crop mix workflow

bool

crop_mix

cross_validation_folds

5

the number of k-folds

int

cross_validation_folds

cross_validation_grouped

False

bool

cross_validation_grouped

cross_validation_parameters

{}

dict

cross_validation_parameters

data_field

None

Field name indicating classes labels in ground_thruth

str

data_field

data_mode_access

gapfilled

choose which data can be accessed in custom features

str

data_mode_access

date_src

None

Date YYYYMMDD of the reference image

str

date_src

date_vhr

None

Date YYYYMMDD of the VHR image

str

date_vhr

deep_learning_parameters

{}

deep learning parameter description is available here

dict

deep_learning_parameters

dempster_shafer_sar_opt_fusion

False

Enable the use of both SAR and optical data to train a model.

bool

dempster_shafer_sar_opt_fusion

dempstershafer_mob

precision

Choose the dempster shafer mass of belief estimation method

str

dempstershafer_mob

dim_red

False

Enable the dimensionality reduction mode

bool

dim_red

douglas

10

Douglas-Peucker tolerance for vector-based generalization

int

douglas

dozip

True

Zip output vector-based classification (OSO-like production)

bool

dozip

enable_autocontext

False

Enable the auto-context processing

bool

enable_autocontext

enable_boundary_fusion

False

Enable the boundary fusion

bool

enable_boundary_fusion

enable_probability_map

False

Produce the probability map

bool

enable_probability_map

enable_sensor_gapfilling

False

Enable or disable gapfilling for landsat 8 and 9 IR data

bool

enable_sensor_gapfilling

end_date

The last date of interpolated image time series : YYYYMMDD format

str

end_date

exogeneous_data

None

Path to a Geotiff file containing additional data to be used in external features

str

exogeneous_data

external_features_flag

False

enable the external features mode

bool

external_features_flag

extract_bands

False

bool

extract_bands

features

[‘NDVI’, ‘NDWI’, ‘Brightness’]

List of additional features computed

list

features

features_from_raw_dates

False

learn model from raw sensor’s date (no interpolations)

bool

features_from_raw_dates

features_path

None

input directory containing features as rasters

str

features_path

fill_missing_dates

False

fill raw data with no data if dates are missing

bool

fill_missing_dates

first_step

None

The step group name indicating where the chain start

str

first_step

force_standard_labels

False

Standardize labels for feature extraction

bool

force_standard_labels

from_rasterdb_resampling_method

nn

output features type choice among gdalwarp.html#cmdoption-gdalwarp-r. Enabled if chain.rasters_grid_path is set

str

from_rasterdb_resampling_method

from_vectordb_resampling_method

near

output features type choice among gdalwarp.html#cmdoption-gdalwarp-r. Enabled if chain.grid is set

str

from_vectordb_resampling_method

full_learn_segment

False

enable the use of entire segment for learning

bool

full_learn_segment

functions

None

function list to be used to compute features

str/list

functions

fusion_options

-nodatalabel 0 -method majorityvoting

OTB FusionOfClassification options for voting method involved if classif_mode is set to ‘fusion’

str

fusion_options

fusionof_all_samples_validation

False

Enable the use of all reference data to evaluate the fusion raster

bool

fusionof_all_samples_validation

fusionofclassification_all_samples_validation

False

Enable the use of all reference data to validate the classification merge

bool

fusionofclassification_all_samples_validation

generate_final_probability_map

False

Enable the mosaicing of probabilities maps.

bool

generate_final_probability_map

grasslib

None

path to grasslib

str

grasslib

grid

None

input grid to fit

str

grid

gridsize

None

number of lines and columns of the serialization process

int

gridsize

ground_truth

None

Absolute path to reference data

str

ground_truth

hermite

10

Hermite Interpolation threshold for vector-based smoothing

int

hermite

higher_stats

False

If True, compute more complexe statistics (Shanon, majority order and difference, etc.)

bool

higher_stats

inland

None

inland water limit shapefile

str

inland

iterate

True

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

bool

iterate

keep_bands

[‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’]

The list of spectral bands used for classification

list

keep_bands

keep_duplicates

True

use ‘rel_refl’ can generate duplicated feature (ie: NDVI), set to False remove these duplicated features

bool

keep_duplicates

keep_runs_results

True

bool

keep_runs_results

keyword_arguments

{}

keyword arguments to be passed to model

dict

keyword_arguments

l5_path_old

None

Absolute path to Landsat-5 images coming from old THEIA format (D*H*)

str

l5_path_old

l8_path

None

Absolute path to Landsat-8 images comingfrom new tiled THEIA data

str

l8_path

l8_path_old

None

Absolute path to Landsat-8 images coming from old THEIA format (D*H*)

str

l8_path_old

l8_usgs_infrared_path

None

Absolute path to Landsat-8 images coming from USGS data

str

l8_usgs_infrared_path

l8_usgs_optical_path

None

Absolute path to Landsat-8 images coming from USGS data

str

l8_usgs_optical_path

l8_usgs_path

None

Absolute path to Landsat-8 images coming from USGS data

str

l8_usgs_path

l8_usgs_thermal_path

None

Absolute path to Landsat-8 images coming from USGS data

str

l8_usgs_thermal_path

last_step

None

The step group name indicating where the chain ends

str

last_step

lcfield

Class

Name of the field to store landcover class in vector-based classification

str

lcfield

lib64bit

None

Path of BandMath and Concatenate OTB executables returning 64-bits float pixel values

str

lib64bit

list_tile

None

List of tile to process, separated by space

str

list_tile

logger_level

INFO

Set the logger level: NOTSET, DEBUG, INFO, WARNING, ERROR, CRITICAL

str

logger_level

max_nn_inference_size

None

maximum batch inference size

int

max_nn_inference_size

maximum_cpu

4

the maximum number of CPU available

int

maximum_cpu

maximum_ram

16.0

the maximum amount of RAM available. (gB)

float

maximum_ram

merge_final_classifications

False

Enable the fusion of classifications mode, merging all run in a unique result.

bool

merge_final_classifications

merge_final_classifications_method

majorityvoting

Indicate the fusion of classification method: ‘majorityvoting’ or ‘dempstershafer’

str

merge_final_classifications_method

merge_final_classifications_ratio

0.1

Percentage of samples to use in order to evaluate the fusion raster

float

merge_final_classifications_ratio

merge_final_classifications_undecidedlabel

255

Indicate the label for undecision case during fusion

int

merge_final_classifications_undecidedlabel

merge_run

False

Enable the fusion of regression mode, merging all run in a unique result.

bool

merge_run

merge_run_method

mean

Indicate the fusion of regression method: ‘mean’ or ‘median’

str

merge_run_method

merge_run_ratio

0.1

Percentage of samples to use in order to evaluate the fusion raster

float

merge_run_ratio

minimum_required_dates

2

required minimum number of available dates for each sensor

int

minimum_required_dates

minsiftpoints

40

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

int

minsiftpoints

minstep

16

Minimal size of steps between bins in pixels

int

minstep

mmu

1000

MMU of output vector-based classification (projection unit),(Default : 0.1 ha)

int

mmu

mode

2

Coregistration mode of the time series

int

mode

mode_outside_regionsplit

0.1

Set the threshold for split huge model

float

mode_outside_regionsplit

model_type

None

machine learning algorthm’s name

str

model_type

module

/path/to/iota2/sources

absolute path for user source code

str

module

no_data_value

-10000

value considered as no_data in features map mosaic (‘i2_features_map’ builder name)

int

no_data_value

no_label_management

maxConfidence

Method for choosing a label in case of fusion

str

no_label_management

nomenclature

None

configuration file which describe nomenclature

configuration file which describe nomenclature

nomenclature

nomenclature_path

None

Absolute path to the nomenclature description file

str

nomenclature_path

number_of_chunks

50

the expected number of chunks

int

number_of_chunks

obia_segmentation_path

None

filename for input segmentation

str

obia_segmentation_path

otb_classifier_options

None

OTB option for classifier.If None, the OTB default values are used

dict

otb_classifier_options

out_worldclim_dtype

float32

output worlclim data type, ie : ‘uint16’, ‘float32’.

np.dtype

out_worldclim_dtype

out_worldclim_rescale_range

None

rescale worldclim data between 0 and max(np.dtype) at run time for RAM usage purpose.

np.dtype

out_worldclim_rescale_range

outprefix

dept

Prefix to use for naming of vector-based classifications

str

outprefix

output_features_pix_type

float

output features type choice among uint8/uint16/int16/uint32/int32/float/double.

str

output_features_pix_type

output_name

None

temporary chunks are written using this name as prefix

str

output_name

output_path

None

Absolute path to the output directory

str

output_path

output_prev_features

None

Path to previous features for crop mix

str

output_prev_features

output_statistics

True

output_statistics

bool

output_statistics

padding_size_x

0

The padding for chunk

int

padding_size_x

padding_size_y

0

The padding for chunk

int

padding_size_y

pattern

None

Pattern of the time series files to coregister

str

pattern

patterns

ALT,ASP,SLP

key name for detect the input images

str

patterns

prec

3

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

int

prec

prev_features

None

Path to a configuration file used to produce previous features

str

prev_features

prod

None

OSO-like output vector (aliases) is produced. Other possible value : carhab

str

prod

proj

None

The projection wanted. Format EPSG:XXXX is mandatory

str

proj

random_seed

None

Fix the random seed for random split of reference data

int

random_seed

rasters_grid_path

None

input grid to fit

str

rasters_grid_path

ratio

0.5

Should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split.

float

ratio

reduction_mode

global

The reduction mode

str

reduction_mode

region_field

region

The column name for region indicator in`region_path` file

str

region_field

region_path

None

Absolute path to a region vector file

str

region_path

region_priority

None

define an order for region intersection

list

region_priority

rel_refl

False

compute relative reflectances by the red band

bool

rel_refl

remove_output_path

True

Before the launch of iota2, remove the content of output_path

bool

remove_output_path

resample

True

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

bool

resample

resampling_bco_radius

2

otb radius for bicubic interpolation.

int

resampling_bco_radius

rssize

20

Resampling size of input classification raster (projection unit)

int

rssize

runs

1

Number of independant runs processed

int

runs

s1_dir

None

Sentinel1 data directory

str

s1_dir

s1_path

None

Absolute path to Sentinel-1 configuration file

str

s1_path

s2_l3a_output_path

None

Absolute path to store preprocessed data in a dedicated directory

str

s2_l3a_output_path

s2_l3a_path

None

Absolute path to Sentinel-2 L3A images (THEIA format)

str

s2_l3a_path

s2_output_path

None

Absolute path to store preprocessed data in a dedicated directory

str

s2_output_path

s2_path

None

Absolute path to Sentinel-2 images (THEIA format)

str

s2_path

s2_s2c_output_path

None

Absolute path to store preprocessed data in a dedicated directory

str

s2_s2c_output_path

s2_s2c_path

None

Absolute path to Sentinel-2 images (Sen2Cor format)

str

s2_s2c_path

sample_augmentation

{‘activate’: False, ‘bins’: 10}

OTB parameters for sample augmentation

dict

sample_augmentation

sample_management

None

Absolute path to a CSV file containing samples transfert strategies

str

sample_management

sample_selection

{‘sampler’: ‘random’, ‘strategy’: ‘all’}

OTB parameters for sampling the validation set

dict

sample_selection

sample_validation

{‘sampler’: ‘random’, ‘strategy’: ‘all’}

OTB parameters for sampling the validation set

dict

sample_validation

samples_classif_mix

False

Enable the second step of crop mix

bool

samples_classif_mix

sampling_validation

False

Enable sampling validation

bool

sampling_validation

seed

1

seed of input raster classification

int

seed

spatial_resolution

[]

Output spatial resolution

list or scalar

spatial_resolution

split_ground_truth

True

Enable the split of reference data

bool

split_ground_truth

srtm_path

None

Path to a directory containing srtm data

str

srtm_path

standardization

True

bool

standardization

start_date

The first date of interpolated image time series : YYYYMMDD format

str

start_date

stats_used

[‘mean’]

list of stats used for train and classification

list

stats_used

statslist

{1: ‘rate’, 2: ‘statsmaj’, 3: ‘statsmaj’}

dictionnary of requested landcover statistics

dict

statslist

step

256

Initial size of steps between bins in pixels

int

step

systemcall

False

If True, use yours gdal lib (cf. bingdal)

bool

systemcall

target_dimension

4

The number of dimension required, according to reduction_mode

int

target_dimension

temporal_resolution

10

The temporal gap between two interpolations

int

temporal_resolution

tile_field

None

column name in ‘grid’ containing tile’s name.

str

tile_field

umc1

None

MMU for the first regularization

int

umc1

umc2

None

MMU for the second regularization

int

umc2

use_additional_features

False

enable the use of additional features

bool

use_additional_features

use_gapfilling

True

enable the use of gapfilling (clouds/temporal interpolation)

bool

use_gapfilling

user_feat_path

None

Absolute path to the user’s features path

str

user_feat_path

validity

None

Input raster of validity

str

validity

validity_threshold

1

threshold above which a training pixel is considered valid

int

validity_threshold

vectorize_fusion_of_classifications

False

flag to inform iota2 to vectorize the fusion of classifications

bool

vectorize_fusion_of_classifications

vhr_path

None

Absolute path to the VHR file

str

vhr_path

worldclim_path

None

Path to a directory containing world clim data

str

worldclim_path

write_outputs

False

write temporary files

bool

write_outputs

write_reproject_resampled_input_dates_stack

True

flag to write of resampled stack image for each date

bool

write_reproject_resampled_input_dates_stack

zonal_vector

None

vector file to compute zonal statistics of classification

str

zonal_vector