Despite technological leaps in our ability to map and monitor coral reef condition, our global tracking of benthic community composition change is still heavily reliant on compiling in-water observations made by snorkelers and divers. Temporal and spatial inconsistencies in field data collection and barriers to integration and data sharing limit our collective understanding of regional-to-global reef health trends.
Here, we introduce the latest advances from ReefCloud. This newly developed open-access platform brings together automated image analyses, statistical modelling, and reporting tools to communicate on the condition of coral reefs more efficiently. Through ReefCloud.ai, machine-learning-enabled automated image analysis can replicate expert observations from photo quadrats with an 85-95% confidence to produce accurate estimations of benthic composition (3% error), at a rate 700-fold faster than manual assessment. Through partnerships with the Global Coral Reef Monitoring Network, government and academic research institutions, and citizen science and community groups, we highlight how ReefCloud is being used to facilitate regional reef monitoring and reporting.