LAUSR

Addressing the global decline of coral reefs requires effective actions from managers, policymakers and society as a whole. Coral reef scientists are therefore challenged with the task of providing prompt and relevant inputs for science-based decision-making. Here, we provide a baseline dataset, covering 1300 km of tropical coral reef habitats globally, and comprised of over one million geo-referenced, high-resolution photo-quadrats analysed using artificial intelligence to automatically estimate the proportional cover of benthic components. The dataset contains information on five major reef regions, and spans 2012–2018, including surveys before and after the 2016 global bleaching event. The taxonomic resolution attained by image analysis, as well as the spatially explicit nature of the images, allow for multi-scale spatial analyses, temporal assessments (decline and recovery), and serve for supporting image recognition developments. This standardised dataset across broad geographies offers a significant contribution towards a sound baseline for advancing our understanding of coral reef ecology and thereby taking collective and informed actions to mitigate catastrophic losses in coral reefs worldwide. Measurement(s) ecosystem • coral reef • composition Technology Type(s) automated image annotation • machine learning Factor Type(s) year of data collection • geographic location Sample Characteristic - Organism Anthozoa • Algae • Porifera Sample Characteristic - Environment marine coral reef biome • marine coral reef fore reef Sample Characteristic - Location Atlantic Ocean • Eastern Australia • Indian Ocean • Southeast Asia • Pacific Ocean • Great Barrier Reef Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13007516