Use the Deep Learning Labeling Tool to create labeled data. The Deep Learning Labeling Tool simplifies the process of drawing rectangle annotations for object detection, or ROIs for pixel segmentation. This is the preferred method for creating object detection rasters or label rasters (collectively called labeled data) for training.

  1. Choose one of the following options to start the Labeling Tool:

    • In the ENVI Toolbox, select Deep Learning > Deep Learning Labeling Tool.

    • In the Deep Learning Guide Map, click one of the following button sequences:

      • Pixel Segmentation > Train a New Pixel Model > Label Rasters.
      • Object Detection > Train a New Object Model > Label Rasters.
      • Grid > Train a New Grid Model > Label Rasters.
  2. Select File > New Project from the Labeling Tool menu bar. The Create New Labeling Project dialog appears.

  3. From the Project Type drop-down list, select one of the following:

    • Pixel Segmentation

    • Object Detection. If you accessed the Labeling Tool from the Guide Map, this option is automatically selected and you cannot change it.

  4. Enter a Project Name.
  5. Click the Browse button next to Project Folder and select an empty folder in which to store project files.
  6. Click OK.

When you create a new project and define your training rasters, ENVI creates subfolders for each training raster that you select as input. Each subfolder contains the annotations used to label features.

ENVI also creates a file named source_raster.json in each subfolder. This is a simplified version (also called a dehydrated form) of the training raster, where all of its information is condensed down to JSON code. If you move the project folder to a different location, the source_raster.json file in each project subfolder tells ENVI where to find the training raster. This way, you do not have to keep track of file locations yourself.

To restore a previously created project, select File > Open Project in the Labeling Tool menu bar. Navigate to your project folder and select the file deep_learning_labeling.json.

After setting up a project, the next step is to define your output classes. Refer to the following topics for additional steps, depending on the option you choose: