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 land cover in images.

Recommended imagery configuration

The recommended imagery configuration is as follows:

• Resolution—Very high resolution (10 centimeters)
• Dynamic range—8-bit
• Bands—Three bands (for example, Red, Green, and Blue)
• Imagery—Orthorectified imagery (both on-the-fly and persisted ortho products work)

Note:

Off-nadir imagery or imagery with a high obliquity angle will not produce suitable results.

If your imagery is already in the recommended imagery configuration, you can skip preprocessing and go to the Classify land cover section.

Classify land cover

Complete the following steps to classify land cover from the imagery:

1. Download the Land Cover Classification (Aerial Imagery) model and add the imagery layer in ArcGIS Pro.
2. Zoom to an area of interest.

   

3. Browse to 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.

   

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. 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 graphics 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.

   

   Note:

   To access the model directly from ArcGIS Pro (supported in ArcGIS Pro 2.7 and later), click the browse button and search for the model.

   

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 0.10.

      The expected raster resolution is 10 centimeters.

   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.

   

7. Click Run.

   The output layer is added to the map.