ImageCLEFcoral Annotation Challenge 2019
The increasing use of structure-from-motion photogrammetry for modelling large-scale environments from action cameras attached to drones has driven the next-generation of visualisation techniques that can be used in augmented and virtual reality headsets. It has also created a need to have such models labelled, with objects such as people, buildings, vehicles, terrain, etc. all essential for machine learning techniques to automatically identify as areas of interest and to label them appropriately. However, the complexity of the images makes it impossible for human annotators to assess the contents of images on a large scale.
Advances in automatically annotating images for complexity and benthic composition have been promising, and we are interested in automatically identify areas of interest and to label them appropriately for monitoring coral reefs. Coral reefs are in danger of being lost within the next 30 years, and with them the ecosystems they support. This catastrophe will not only see the extinction of many marine species, but also create a humanitarian crisis on a global scale for the billions of humans who rely on reef services. By monitoring the changes and composition of coral reefs we can help prioritise conservation efforts.
The data for this task originates from a growing, large-scale collection of images taken from coral reefs around the world as part of a coral reef monitoring project with the Marine Technology Research Unit at the University of Essex (currently containing over 2TB of image data of benthic reef structure).