ReefCloud

ReefCloud is a digital tool that supports underwater coral reef benthic data collection and analysis, allowing the world&#39;s coral reef monitoring community to work together in real time to improve the storage, sharing and interpretation of in-water ecological observations that help us track global reef health. Co-funded by the Australian Department of Foreign Affairs and Trade (DFAT) and the Australian Institute of Marine Science (AIMS), ReefCloud provides an end-to-end solution for integrating, synthesising, reporting and communicating coral reef monitoring data using technology developed at AIMS.
The online platform, ReefCloud.ai, leverages digital technologies (including cloud-based computing, Artificial Intelligence and Bayesian Statistics) to make coral reef data more integratable and sharable, with the ultimate goal of strengthening the linkage between underwater ecological scientific insights and data-driven actions to manage coral reefs. 
Key innovations - what does ReefCloud do?
Collect and manage benthic monitoring images
AIMS staff and researchers outside of AIMS are able to upload underwater digital photos from monitoring surveys along with associated data - location, date, habitat, depth - to the ReefCloud Data Portal to rapidly assess the ecological condition of coral reefs. An automated pipeline secures and organises this data, making ReefCloud a global cloud-based repository for benthic imagery, and there are options to share data at multiple levels to foster collaborations between scientists and organisations for improved insights into regional and global reef health status.
Analyse and automatically annotate images using AI
ReefCloud employs machine-learning from trained experts to automate image analysis, extracting relevant information on benthic composition quickly and efficiently. This helps researchers to draw out advanced taxonomic detail from their submitted images, increase data analysis efficency and validate automated methods.
How does this work?
The AIMS Long Term Monitoring Program (LTMP) performs annual, image-based surveys of 80 reefs on Australia&rsquo;s Great Barrier Reef. This dataset consists of millions of quality-controlled point annotations made by expert coral reef ecologists. AIMS use this large, high-quality dataset to train a machine learning model. As new images are uploaded to ReefCloud by external users, the trained model converts them into feature vectors which are classified into coral reef categories using smaller, faster classification machine learning models.
Users need only label a part of their dataset to allow the model to pick out the specifics of the new images and provide accurate labels. Users can then output a spreadsheet that contains the proportion of different reef species detected in each submitted photo - vital data for understanding reef condition.
ReefCloud crops newly imported images into &quot;patches&quot; centred on sampling points (number and distribution per image defined by user). The algorithm views all 65,000 pixels within each 256 by 256 pixel patch, extracting information on colours and contrasts and neighbours of each pixel, and compressing all that information into a 128-digit &ldquo;feature vector&rdquo;. ReefCloud assigns a label to a point, that feature vector is associated with a certain class, defined by users during training. The machine that runs over the AI performs a classification on remaining non-human annotated points by inferencing using the numbers. This is faster than using traditional machine learning where imagery is directly compared &ndash; but has the same accuracy. The difference in time for &ldquo;inferencing&rdquo; between these two methodologies is significant &ndash; minutes for a number vs hours to days for pixels. Even though it takes a little additional time to create the feature vector for each point annotated, the benefit is ReefCloud can re-inference entire datasets easily whenever required.
Synthesise insights and view automatically updated reports
Interactive dashboards, report cards and data access tools enable rapid interpretation, reporting and communication on coral reef conditions across geographies. 
Data are modelled for presentation on the ReefCloud Public Reporting Dashboard.
What data are available on the Public Dashboard and how is it generated?
All publicly available data from ReefCloud from a specific region is compiled into a single data set that includes information about the sampling source, depth, date, location and number of photo points identified as belonging to each of 12 broad benthic groups (Hard Coral, Soft Coral, Macroalgae, Turf Algae, Crustose Coralline Algae, Cyanobacteria, Seagrass, Hard Substrate, Rubble, Soft Sediment, Other Invertebrates and Other). These data inform broad spatial-scale statistical models, which predict annual hard coral cover and macroalgae cover (median and upper/lower credibility intervals) across all known coral reefs (both monitored and unmonitored) in the region by incorporating a spatial grid and information about the environment (currently cyclone activity and degree heating weeks).
ReefCloud employs two different models to provide summary statistics and trends for an indivudal monitoring site vs a region with a broader spatial scale. These two models serve two different purposes. The site-specific models provide an estimate of the cover at a single site and are informated only by data submitted from that local site. The broader spatial scale models provide estimates of cover at all sites regardless of whether photos exist inside ReefCloud or not. Importantly, these models are informed by all available monitoring and environmental data for the entire region and thus, the predictions for any given site and the result of both the observed data extracted from photos for that site as well as the more general patterns in its neighbourhood. Consequently, it is possible that the two different models may yield slightly different esrunares if thet are both compared for a single site or an area that only had a single monitored site.

Organization: Australian Ocean Data Network