Whitebox Workflows for Python
Whitebox Workflows for Python Pro
Whitebox Worfklows for Python Pro (WbW-Pro) contains all of the same functions as the standard WbW product and adds the 75+ tools found in the Whitebox Toolset Extension (WTE), allowing users to access this powerful functionality in the same great geo-processing environment. WbW-Pro also adds several functions that aren’t available in other Whitebox products, such as the improved_ground_point_filter, nibble, sieve, and ridge_and_valley_vectors functions. Just like the free version of WbW, each of the WbW-Pro functions are also accessible through our Whitebox Geospatial developed QGIS plugin, an easy user interface for the WbW library.
Like all of our products, WbW-Pro has transparent pricing. A single-user license is $400USD and each additional user is only $60 more. We also offer an academic discount for qualified customers.
WbW-Pro List of Tools
WbW-Pro contains each of the functions listed below. Click on each function name to view the help documentation, parameters, and usage instructions. Although you are able to install the the WbW-Pro using the same convenient pip install as WbW, you can’t use WbW-Pro functions until you purchase a valid license. All purchases include free updates to future WbW versions within the license period (annual or perpetual), including any new functions.
DEM Processing
LiDAR Analysis
Machine Learning
Hydrology Analysis
Remote Sensing and GIS
Agriculture
DEM Processing
- accumulation_curvature Calculates accumulation curvature from an input DEM.
- assess_route Assesses a route for slope, elevation, and visibility variation.
- average_horizon_distance New! Calculates the average distance to the horizon for all grid cells in an input DEM.
- breakline_mapping Automatically maps breaklines from an input DEM.
- curvedness Calculates curvedness from an input DEM.
- dem_void_filling Fills the void areas of a DEM using another fill DEM data set.
- difference_curvature Calculates difference curvature from an input DEM.
- generating_function Calculates generating function from an input DEM.
- horizon_area New! Calculates the area of the horizon polygon centered on each point in an input DEM.
- horizontal_excess_curvature Calculates horizontal excess curvature from an input DEM.
- local_hypsometric_analysis Calculates the hypsometric integral from the elevation distribution contained within the local neighbourhood surrounding each grid cell in an input DEM.
- low_points_on_headwater_divides Locates saddle points along ridges within a DEM.
- multiscale_curvatures Calculates several multiscale curvatures and curvature-based indices from an input DEM.
- openness Calculates the topographic openness index from an input DEM.
- ridge_and_valley_vectors New! Extracts ridge and channel vectors from an input digital elevation model (DEM).
- ring_curvature Calculates ring curvature from an input DEM.
- rotor Calculates rotor from an input DEM.
- shadow_animation Creates an animated GIF of shadows based on an input DEM.
- shadow_image Creates a raster of shadow areas based on an input DEM.
- shape_index Calculates shape index from an input DEM.
- skyline_analysis New! Performs a skyline analysis for one or more observation points based on the terrain of an underlying digital elevation model (DEM).
- sky_view_factor New! Calculates the sky-view factor from an input DEM.
- slope_vs_aspect_plot Creates a slope-aspect relation plot from an input DEM.
- topographic_position_animation Creates an animated GIF of multi-scale local topographic position.
- topo_render Creates a pseudo-3D rendering from an input DEM, for the purpose of effective topographic visualization.
- unsphericity Calculates unsphericity curvature from an input DEM.
- vertical_excess_curvature Calculates vertical excess curvature from an input DEM.
LiDAR Analysis
- classify_lidar Classifies LiDAR point clouds into ground, building, and vegetation points.
- colourize_based_on_class Sets the RGB values of a LiDAR point cloud based on the point classification values.
- colourize_based_on_point_returns Sets the RGB values of a LiDAR point cloud based on the point return values.
- filter_lidar Filters points within a LiDAR point cloud based on point properties.
- filter_lidar_by_percentile New! Extracts a subset of points from a LiDAR point cloud that correspond to a user-specified percentile of the points within the local neighbourhood.
- filter_lidar_by_reference_surface New! Extract a subset of points from a LiDAR point cloud that satisfy a query relation with a user-specified raster reference surface.
- improved_ground_point_filter New! Identifies and extracts ground points from an input LiDAR point cloud.
- lidar_contour Creates a vector contour coverage from an input LiDAR point file.
- lidar_eigenvalue_features Calculate eigenvalue-based metrics from a LiDAR point cloud, including linearity, planarity, sphericity, omnivariance, eigentropy, and others.
- lidar_point_return_analysis Performs a quality control check on the return values of points in a LiDAR file.
- lidar_sibson_interpolation Interpolates one or more LiDAR tiles using Sibson’s natural neighbour method.
- modify_lidar Modify points within a LiDAR point cloud based on point properties.
- recover_flightline_info Associates LiDAR points by their flightlines.
- smooth_vegetation_residual Smoothes the residual roughness due to vegetation cover in LiDAR DEMs.
- sort_lidar Sorts the points in a LiDAR file based on point properties.
- split_lidar Splits LiDAR points up into a series of new files based on their properties.
Machine Learning
- dbscan Performs a DBSCAN-based unsupervised clustering operation.
- knn_classification Performs a k-nearest neighbour classification using training site polygons/points and predictor rasters.
- knn_regression Performs a k-nearest neighbour regression analysis using training site points and predictor rasters.
- logistic_regression Performs a logistic regression analysis using training site polygons/points and predictor rasters.
- random_forest_classification_fit Performs a random forest classification using training site polygons/points and predictor rasters.
- random_forest_classification_predict Performs a random forest classification prediction based on a fitted model.
- random_forest_regression_fit Performs a random forest regression analysis using training site points and predictor rasters.
- random_forest_regression_predict Applies a random forest regression prediction based on a fitted model.
- svm_classification Performs a SVM classification using training site polygons/points and predictor rasters.
- svm_regression Performs a SVM regression analysis using training site points and predictor rasters.
Hydrology Analysis
- depth_to_water Calculates cartographic depth-to-water (DTW) index.
- hydrologic_connectivity Calculates two indices related to hydrologic connectivity within catchments, the downslope unsaturated length and the upslope disconnected saturated area.
- prune_vector_streams Prunes the smallest branches of a vector stream network based on a threshold in link magnitude.
- river_centerlines Maps river centerlines from an input water raster.
- topological_breach_burn Performs a specialized form of stream burning.
Remote Sensing and GIS
- canny_edge_detection Performs a Canny edge-detection filter on an input image.
- evaluate_training_sites Inspects the overlap in spectral signatures of training sites for various classes.
- fix_dangling_arcs Fixes undershot and overshot arcs, two common topological errors, in an input vector lines file.
- generalize_classified_raster Generalizes a raster containing class or object features by removing small features.
- generalize_with_similarity Generalizes a raster containing class features by reassigning the identifier values of small features to those of neighbouring features based on similarity in spectral space.
- image_segmentation Performs a region-growing based segmentation on a set of multi-spectral images.
- image_slider Creates an image slider from two input images.
- inverse_pca Performs an inverse principal component analysis on a series of input component images.
- min_dist_classification Performs a supervised minimum-distance classification using training sites and multi-spectral images.
- nibble New! Assigns areas within an input class map raster that are coincident with a mask the value of their nearest neighbour.
- parallelepiped_classification Performs a supervised parallelepiped classification using training sites and multi-spectral images.
- phi_coefficient Performs a binary classification accuracy assessment.
- piecewise_contrast_stretch Performs a piecewise contrast stretch on an input image.
- remove_raster_polygon_holes Remove raster polygon holes or “donut-holes”, from raster polygons.
- sieve New! Removes individual objects in a class map that are less than a threshold area, in grid cells.
Agriculture
- reconcile_multiple_headers Adjusts the crop yield values for data sets collected with multiple headers or combines.
- remove_field_edge_points Remove or flag most of the points along the edges from a crop yield data set.
- recreate_pass_lines Approximates the harvester pass lines from yield points.
- yield_filter Filters crop yield values of point data derived from combine harvester yield monitors.
- yield_map Creates a segmented-vector polygon yield map from a set of harvester points.
- yield_normalization Normalizes the yield points for a field.