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Introduction to the model

Banner image for the model

This deep learning model is used to detect trees in high-resolution drone or aerial imagery. Tree detection can be used for applications such as vegetation management, forestry, urban planning, and so on. High-resolution aerial and drone imagery can be used for tree detection because of its high spatiotemporal coverage.

This deep learning model is based on DeepForest and has been trained on data from the National Ecological Observatory Network.

License requirements

To complete this workflow, the following are the license requirements:

  • ArcGIS DesktopArcGIS Image Analyst extension for ArcGIS Pro
  • ArcGIS EnterpriseArcGIS Image Server
  • ArcGIS OnlineArcGIS Image for ArcGIS Online

Model details

This model has the following characteristics:

  • Input—8-bit, 3-band high-resolution aerial imagery.
  • Output—Feature class containing detected trees.
  • Compute—This workflow is compute intensive, and a GPU with compute capability of 6.0 or higher is recommended.
  • Applicable geographies—This model is designed to work well across the United States.
  • Architecture—This model is based on the DeepForest Python package and uses the RetinaNet model architecture implemented in Torchvision.
  • Accuracy metrics—This model has precision and recall scores of 0.66 and 0.79, respectively.

Access and download the model

Download the Tree Detection—USA pretrained model from ArcGIS Living Atlas of the World. Alternatively, access the model directly from ArcGIS Pro, or consume it in ArcGIS Image for ArcGIS Online.

  1. Browse to ArcGIS Living Atlas of the World.
  2. Sign in with your ArcGIS Online credentials.
  3. Search for Tree Detection and open the item page from the search results.
  4. Click the Download button to download the model.

    You can use the downloaded .dlpk file directly in ArcGIS Pro, or upload and use it in ArcGIS Enterprise. Additionally, you can fine-tune the pretrained model if necessary.

Release notes

The following are the release notes:


May 2022

  • First release of Tree Detection