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Use the model

You can use this model in the Classify Pixels Using Deep Learning tool available in the Image Analyst toolbox in ArcGIS Pro. Follow the steps below to use the model for classifying human settlements in images.

Supported imagery

This model can be used with multispectral Landsat 8 (Collection 1 Level-1) imagery in the form of a mosaic dataset or image service.

Ensure that you create a mosaic dataset using the Manage Landsat 8 imagery tool. Once created, you can use the multispectral mosaic dataset generated by the tool. This mosaic dataset can also be published as an image service and used as an input.

Ensure that the processing template is set to None.

Classify human settlements

Use the following steps to classify human settlements from the imagery:

  1. Download the Human Settlements Classification (Landsat 8) model and add the imagery layer in ArcGIS Pro.
  2. Zoom to an area of interest.
    Zoomed to an area of interest
  3. Browse to Tools on the Analysis tab.
    Tools on the Analysis tab
  4. Click the Toolboxes tab in the Geoprocessing pane, select Image Analyst Tools, and browse to the Classify Pixels Using Deep Learning tool under Deep Learning.
    Classify Pixels Using Deep Learning tool
  5. Set the variables on the Parameters tab as follows:
    1. Input Raster—Select the imagery.
    2. Output Classified Raster—Set the output feature class that will contain the classification results.
    3. Model Definition—Select the pretrained or fine-tuned model .dlpk file.
    4. Model Arguments (optional)—Change the values of the arguments if required.
      • padding—Number of pixels at the border of image tiles from which predictions are blended for adjacent tiles. Increase its value to smooth the output while reducing edge artifacts. The maximum value of the padding can be half of the tile size value.
      • batch_size—Number of image tiles processed in each step of the model inference. This depends on the memory of your graphic card.
      • predict_background—If set to True, background class is also classified.
      • test_time_augmentation—Performs test time augmentation while predicting. If true, predictions of flipped and rotated variants of the input image will be merged into the final output.
      • tile_size—The width and height of image tiles into which the imagery is split for prediction.
    Classify Pixels Using Deep Learning Parameters tab
  6. Set the variables on the Environments tab as follows:
    1. Processing Extent—Select Current Display Extent or any other option from the drop-down menu.
    2. Cell Size (required)—Set the value to 30.

      The expected raster resolution is 30 meters.

    3. Processor Type—Select CPU or GPU.

      It is recommended that you select GPU, if available, and set GPU ID to specify the GPU to be used.

    Classify Pixels Using Deep Learning Environments tab
  7. Click Run.

    The output layer is added to the map.

    Results showing classified human settlements