Semantic inference scf

operators.ml3d.semantic_inference_scf(client, data_in_path='file.laz', class_names='1,2,3,4,5,6,7,8', feature_names='red,green,blue', point_names='x,y,z', label_name='classification', feature_dimensions='12,48,96,192,384', batch_size=2, results_labels_path='result.labels', in_model_parameters_path='results/Log_2022-11-10_11-42-05', results_probabilities_path='result_probs.npy', number_of_votes=5, instance_type='x2large')

semantic_inference_scf( client,

data_in_path=’file.laz’,

class_names=’1,2,3,4,5,6,7,8’,

feature_names=’red,green,blue’,

point_names=’x,y,z’,

label_name=’classification’,

feature_dimensions=’12,48,96,192,384’,

batch_size=2,

results_labels_path=’result.labels’,

in_model_parameters_path=’results/Log_2022-11-10_11-42-05’,

results_probabilities_path=’result_probs.npy’,

number_of_votes=5,

instance_type=’x2large’ )

Parameters:

• data_in_path – path to file that contains the training data

• class_names – comma separated list of class names. Class 0 is always given and is used to denote unlabeled points.

• feature_names – comma separated list of features that are provided

• point_names – comma separated list of point identifiers in (las/laz)

• label_name – label name for (las/laz)

• feature_dimensions – feature dimensions

• batch_size – batch_size

• results_labels_path – path to labels

• in_model_parameters_path – path to model

• results_probabilities_path – path to probabilities

• number_of_votes – number of votes to vote for a class

• instance_type – type of cloud instance used for processing