Label | Explanation | Data Type |
Input Training Data
| The point cloud training data (*.pctd file) that will be used to train the classification model. | File |
Output Model Location
| An existing folder that will store the new directory containing the deep learning model. | Folder |
Output Model Name
| The name of the output Esri model definition file (*.emd), deep learning package (*.dlpk), and the directory that will be created to store them. | String |
Pre-trained Model
(Optional) | The pretrained model that will be refined. When a pretrained model is provided, the input training data must have the same attributes, class codes, and maximum number of points that were used by the training data that generated this model. | File |
Attribute Selection
(Optional) | Specifies the point attributes that will be used to train the model. Only the attributes that are present in the point cloud training data will be available. No additional attributes are included by default.
| String |
Minimum Points Per Block
(Optional) | The minimum number of points that must be present in a given block for it to be used when training the model. The default is 0. | Long |
Class Remapping
(Optional) | Defines how class code values will map to new values before training the deep learning model. | Value Table |
Class Codes Of Interest
(Optional) | The class codes that will be used to filter the blocks in the training data. When class codes of interest are specified, all other class codes are remapped to the background class code. | Long |
Background Class Code
(Optional) | The class code value that will be used for all other class codes when class codes of interest have been specified. | Long |
Class Description
(Optional) | The descriptions of what each class code in the training data represents. | Value Table |
Model Selection Criteria
(Optional) | Specifies the statistical basis that will be used to determine the final model.
| String |
Maximum Number of Epochs
(Optional) | The number of times each block of data will be passed forward and backward through the neural network. The default is 25. | Long |
Iterations Per Epoch (%)
(Optional) | The percentage of the data that will be processed in each training epoch. The default is 100. | Double |
Learning Rate
(Optional) | The rate at which existing information will be overwritten with new information. If no value is provided, the optimal learning rate will be extracted from the learning curve during the training process. This is the default. | Double |
Batch Size (Optional) | The number of training data blocks that will be processed at any given time. The default is 2. | Long |
Stop training when model no longer improves (Optional) | Specifies whether the model training will stop when the metric specified in the Model Selection Criteria parameter does not register any improvement after five consecutive epochs.
| Boolean |
Learning Rate Strategy
(Optional) | Specifies how the learning rate will be modified during training.
| String |
Model Architecture
(Optional) | Specifies the neural network architecture that will be used to train the model. When a pretrained model is specified, the architecture used for creating the pretrained model will be automatically set.
| String |
Derived Output
Label | Explanation | Data Type |
Output Model | The resulting model generated by this tool. | File |
Output Model Statistics | The .csv file containing the precision, recall, and F1 scores for each class code and epoch. | Text File |
Output Epoch Statistics | The .csv file containing the training loss, validation loss, accuracy, precision, recall, and F1 scores obtained in each epoch. | Text File |