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Introduction to the model

Banner image for the model

Shipwrecks are a potential threat to the ships passing by on the surface. Marking them manually is a complex and time-consuming task. Deep learning can be used to significantly optimize and automate this task. You can use this model as is or fine-tune it to adapt it to your own data and geography.

This model is used to detect shipwrecks in high-resolution (50 centimeter) Bathymetric Attributed Grid (BAG) data.

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—BAG data.
  • Output—Feature class containing detected shipwrecks as polygons.
  • 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 on high-resolution BAG files for any marine geography.
  • Architecture—This model uses the Mask R-CNN model architecture implemented in ArcGIS API for Python.
  • Accuracy metrics—This model has an average precision score of 0.92.

Access and download the model

Download the Shipwreck Detection pretrained model from ArcGIS Living Atlas of the World. Alternatively, access the model directly from ArcGIS Pro using the Detect Shipwrecks ArcGIS Pro Project Template, 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 Shipwreck 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 use 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:


February 2021

  • First release of Shipwreck Detection