Experimental
Supervised Random Forest classification for multisource raster features using point/polygon training data.
remote_sensing classification random_forest legacy-port
| Name | Description | Required | Default |
|---|---|---|---|
inputs | Array of single-band input rasters. | Required | ['band1.tif', 'band2.tif', 'band3.tif'] |
training_data | Point/polygon vector training data path. | Required | training.shp |
class_field | Class field in training_data attributes. | Required | class |
scaling | Feature scaling mode: none (default), normalize, standardize. | Optional | none |
n_trees | Number of trees in the forest (default 200). | Optional | 200 |
min_samples_leaf | Minimum number of samples required at a leaf node (default 1). | Optional | 1 |
min_samples_split | Minimum number of samples required to split an internal node (default 2). | Optional | 2 |
output | Optional output raster path. | Optional | — |
Run random forest classification on multiband predictors.
wbe.random_forest_classification(class_field='class', inputs=['band1.tif', 'band2.tif', 'band3.tif'], n_trees=300, output='rf_classification.tif', scaling='standardize', training_data='training.shp')