About CoralNet

CoralNet is a repository and a resource for benthic images analysis. The site implements computer vision algorithms which allow fully- and semi- automated annotation. It also serves as a repository and collaboration platform.

We have recently launced the Beta site! Check out the release-notes for details.


Global and local stressors have caused a rapid decline of coral reefs across the world. To monitor the changes and take appropriate action large spatio-temporal surveys are needed. Data collection speeds are typically sufficient to meet this need but the subsequent image analysis remains slow as manual inspection of each photo is required. This creates a 'manual annotation bottleneck'.

CoralNet reduces this bottleneck by allowing modern computer vision algorithms to be deployed alongside human experts. Often 50-100% automation can be achieved with minimal reduction in the quality of the final data-product. CoralNet, by its nature, also provides a platform for collaboration & sharing of data.

The site

The CoralNet website consist of several modules outlined below. For some (slightly outdated) information, check out our Vimeo channel.

Source: Main organizational element for a benthic survey or image "source". Here you specify your labelset, your privacy settings, and invite collaborators.

Labelset: Specify what labels you want to use in your analysis. Choose from a set of existing labels or create your own.

Import: Upload images, metadata and archived annotations to the site.

Annotation: Annotate your image right in the web browser using a point count interface. When enough images are manually annotated, an automated annotator is trained. This automated annotator is integrated directly into the annotation tool and makes the remaining annotation work easier.

Data Policy

CoralNet are committed to protecting the data privacy of its users, and offer two privacy options.


All your images and annotations are private but the source location is shown on the world map along with the name, description, affiliation and the total number of images. We will also show thumbnails extracted from annotated image locations as part of our label browse tool.


All your source images and annotation data are available for the public to browse and download (including original images in full resolution). However, only members of the source can add or edit content.


CoralNet development is support by the NSF Computer Vision Coral Ecology grant #ATM-0941760 and by the National Oceanic and Atmospheric Administration (NOAA) grant No. NA10OAR4320156. The following papers are relevant to the development of CoralNet. We ask that you cite the appropriate papers if you use CoralNet in your work.


  • Oscar Beijbom - Project Manager [www]
  • Stephen Chan - Lead Developer
  • David Kriegman - PI [www]
  • TBD - Labelset Committee
  • TBD - Labelset Committee
  • Serge Belongie - Academic advisor [www]
  • David Kline - Academic advisor [www]
  • Ben Neal - Academic advisor [www]
  • Gregory Mitchell - Academic advisor [www]
  • Devang Sampat - developer (Alumni)
  • Andrew Hu - developer (Alumni)
  • Jeff Sandvik - developer (Alumni)


  • January 2017: Our beta launch featured in phys.org [www].
  • September 2016: CoralNet featured in the GTC keynote (at 0:0:52) [www].
  • August 2016: CoralNet featured in the Nature Toolbox section [www].
  • June 2016: NVIDIA blog post about my work on deep learning for coral ecology [www].
  • Nov 2014: Jonathan Cohen at NVIDIA highlights CoralNet in his talk at the SuperComputer conference. CoralNet section starts at 9.30 [www].
  • May 2014: Destin at SmarterEveryDay follows the data collected by the Catlin Seaview Survey all the way to CoralNet [www].
  • September 2013: Greenwire covers CoralNet [www].