i2_vectorization

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

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

Notes

output_path

Absolute path to the output directory.It is recommended to have one directory per run of the chain

simplification

Name

Default Value

Description

Type

Mandatory

Name

angle

True

If True, smoothing corners of pixels (45°)

bool

False

angle

bingdal

None

path to GDAL binaries

str

False

bingdal

blocksize

2000

block size to split raster to prevent Numpy memory error

int

False

blocksize

chunk

10

Number of chunks for statistics computing

int

False

chunk

classification

None

Input raster of classification

str

True

classification

clipfield

None

field to identify distinct areas

str

False

clipfield

clipfile

None

vector-based file to clip output vector-based classification

str

False

clipfile

clipvalue

None

value of field which identify distinct areas

int

False

clipvalue

confidence

None

Input raster of confidence

str

True

confidence

douglas

10

Douglas-Peucker tolerance for vector-based generalization

int

False

douglas

dozip

True

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

bool

False

dozip

grasslib

None

path to grasslib

str

False

grasslib

gridsize

None

Number of lines and columns of serialization process

int

False

gridsize

hermite

10

Hermite Interpolation threshold for vector-based smoothing

int

False

hermite

higher_stats

False

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

bool

False

higher_stats

inland

None

Inland water limit shapefile

str

False

inland

lcfield

Class

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

str

False

lcfield

lib64bit

None

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

str

False

lib64bit

mmu

1000

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

int

False

mmu

nomenclature

None

configuration file which describe nomenclature

str

False

nomenclature

outprefix

dept

Prefix to use for naming of vector-based classifications

str

False

outprefix

prod

None

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

str

False

prod

rssize

20

Resampling size of input classification raster (projection unit)

int

False

rssize

seed

1

Seed of input raster classification

int

False

seed

statslist

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

dictionnary of requested landcover statistics

dict

False

statslist

systemcall

False

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

bool

False

systemcall

umc1

None

MMU for first regularization

int

False

umc1

umc2

None

MMU for second regularization

int

False

umc2

validity

None

Input raster of validity

str

True

validity

vectorize_fusion_of_classifications

False

flag to inform iota2 to vectorize the fusion of classifications

bool

False

vectorize_fusion_of_classifications

zonal_vector

None

vector file to compute zonal statistics of classification

str

False

zonal_vector

Notes

bingdal

Some GDAL lib versions (automatically set up with iota2) are not efficient to handle topology errors, use yours !

blocksize

Numpy memory error may occur for large areas during serialization process. Split in sub-rasters prevents memory error

chunk

Number of chunks (groups of vector-based features) for parallel computing landcover statistics

classification

This parameter is automatically set if the configuration file use the classification and vectorization builders

clipfield

field to identify distinct geographical/administrativeareas (cf. “clipfile” parameter)

clipfile

vector-based file can contain more than one feature (geographical/administrative areas). An output vector-based classification is produced for each feature (cf. ‘clipfield’ parameter).

clipvalue

output vector-based classification is only produced on the specific area (clipfield=clipvalue in clipfile) (cf. ‘clipfield’ parameter). If None, all areas are produced.

confidence

This parameter is automatically set if the configuration file use the classification and vectorization builders

grasslib

Some functions of GRASS GIS software are used to vectorize, simplify and smooth vector layer. This path corresponds to GRASS install folder

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

lib64bit

Band math and concatenate OTB executables with 64 bits capabilities (only for large areas where clumps number > 2²³ bits for mantisse)

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
               }
               ...
        }
}

outprefix

Naming of vector-based classifications is as following : prefix_clipvalue

rssize

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

seed

This parameter is usefull to vectorize one specific output classification seed

statslist

Different landcover statistics can be computed for vector-based classification file. A python-like dictionnary must be provided. This is the OSO-like statistics :{1: “rate”, 2: “statsmaj”, 3: “statsmaj”}

Where:
  • {1: “rate”} : rates of classification classes are computed for each polygon

  • {2: “statsmaj”} : descriptive stats of classifier confidence are computed for each polygon by using only majority class pixels

  • {3: “statsmaj”} : descriptive stats of sensor validity are computed for each polygon by using only majority class pixels

list of available statistics :
  • stats : mean_b, std_b, max_b, min_b

  • statsmaj : meanmaj, stdmaj, maxmaj, minmaj of maj. class

  • rate : rate of each pixel value (classe names)

  • stats_cl : mean_cl, std_cl, max_cl, min_cl of one class

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

validity

This parameter is automatically set if the configuration file use the classification and vectorization builders

vectorize_fusion_of_classifications

This flag is only useful if the vectorization is chained with classification workflow

zonal_vector

Compute the zonal statistics on the geometries ofa user-provided vector file. Zonal statistics arecomputed on classification, confidence and rasterinput rasters

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