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 |