Use the model

You can use this model in the Detect Objects Using Deep Learning tool available in the Image Analyst toolbox in ArcGIS Pro.

Complete the following steps to use the Tree Detection pretrained model:

1. Download the model and add the imagery layer in ArcGIS Pro.
2. Zoom to an area of interest.

   

3. Click the Analysis tab and browse to Tools.

   

4. In the Geoprocessing pane, click Toolboxes and expand Image Analyst Tools. Select the Detect Objects Using Deep Learning tool under Deep Learning.

   

5. On the Parameters tab, set the variables as follows:
   1. Input Raster—Select the imagery.
   2. Output Feature Class—Set the output feature class that will contain the detected objects.
   3. Model Definition (optional)—Select the pretrained 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.
      • threshold—The detections with a confidence score higher than this threshold are included in the result. The allowed value ranges from 0 to 1.0.
      • nms_overlap—The ratio of overlap between bounding boxes with which to filter detections with lower confidence scores.
      • batch_size—Number of image tiles processed in each step of the model inference. This depends on the memory of your graphics card.
      • exclude_pad_detections—If true, it allows the model to ignore padded areas in images, enhancing precision by focusing on relevant content, while setting it to false includes these areas, which may introduce noise and irrelevant detections.
      • test_time_augmentation—Performs test time augmentation while predicting. This is a technique used to improve the robustness and accuracy of model predictions. It involves applying data augmentation techniques during inferencing, which means generating multiple slightly modified versions of the test data and aggregating the predictions. If true, predictions of flipped and rotated orientations of the input image will be merged into the final output and their confidence values are averaged. This may cause the confidence to fall below the threshold for objects that are only detected in a few orientations of the image.
   5. Non Maximum Suppression—Optionally, check the check box to remove the overlapping features with lower confidence.

      If checked, do the following:

      • Set Confidence Score Field.
      • Set Class Value Field (optional).
      • Set Maximum Overlap Ratio (optional).

   

   Note:

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

   

6. On the Environments tab, set the variables 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 0.25.

      The expected raster resolution is 0.1–0.25 meters.

   3. Processor Type—Select CPU or GPU as needed.

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

   

7. Click Run.

   The output layer is added to the map.