Target Point Cloud
The point cloud that will be classified.
|LAS Dataset Layer|
Input Model Definition
The input Esri model definition file (*.emd) or deep learning package (*.dlpk) that will be used to classify the point cloud. A web address for a deep learning package that is published on ArcGIS Online or ArcGIS Living Atlas can also be used.
The class codes from the trained model that will be used to classify the input point cloud. All classes from the input model will be used by default unless a subset is specified.
Existing Class Code Handling
Specifies how the editable points from the input point cloud will be defined.
Existing Class Codes
The classes for which points will be edited or have their original class code designation preserved based on the Existing Class Code Handling parameter value.
Specifies whether statistics will be computed for the .las files referenced by the LAS dataset. Computing statistics provides a spatial index for each .las file, which improves analysis and display performance. Statistics also enhance the filtering and symbology experience by limiting the display of LAS attributes, such as classification codes and return information, to values that are present in the .las file.
The polygon boundary that defines the subset of points to be processed from the input point cloud. Points outside the boundary features will not be evaluated.
Specifies whether the LAS dataset pyramid will be updated after the class codes are modified.
The raster surface that will be used to provide relative height values for each point in the point cloud data. Points that do not overlap with the raster will be omitted from the analysis.
Excluded Class Codes
The class codes that will be excluded from processing. Any value in the range of 0 to 255 can be specified.
The point cloud data blocks that will be simultaneously processed by the neural network during the inferencing operation. When no value is specified, the optimal batch size will be calculated based on the available GPU memory. The amount of GPU memory used by a given block depends on the block point limit and point cloud attributes required by the model.
|Output Point Cloud|
The point cloud that was classified by the deep learning model.