The team aligned to bring together the speed of cloud computing, the automation provided by AI and the decision-making capabilities of data analytics – all into a single solution built with scientists’ needs and uses in mind. Over the course of 18 months, they worked with the Queensland University of Technology (QUT), coral reef scientists, conservation practitioners and technology experts across various organizations to design an open access tool called ReefCloud.
ReefCloud is a cloud-based web platform hosted on Amazon Web Services that uses computer vision to analyze reef photos, and automatically extract, annotate and share data points like coral cover or reef composition. After analyzing a photo, the platform automatically adds the data to a dashboard, designed with a user-friendly interface. The dashboard translates the data into valuable insights and recommendations so conservationists can quickly understand what the data says about reef and coral health. And perhaps most importantly, the platform enables democratized knowledge. It standardizes the data for rapid interpretation, reporting and communication on reef conditions across languages, geographies and scientific methodologies.
The tool’s AI model was trained using data from AIMS’ own reef-monitoring program, which contains thousands of images and data points from the Great Barrier Reef. But the computer vision model was just one part of the equation—ReefCloud also needs people.
ReefCloud is built for collaboration. It allows the world’s reef-monitoring community to upload photos and work together in real time. And the algorithm learns with each new image added—so the more people who use ReefCloud, the bigger the impact it can have.