Coral reef monitoring is data-intensive work. A single survey can produce thousands of underwater photos, fish counts, bleaching observations, and benthic point estimates. Turning all of that into trustworthy, comparable data has long been one of the hardest parts of reef science.
Over the last decade, two open-source platforms have been developed to tackle different parts of that bottleneck. CoralNet, developed at UC San Diego and the Scripps Institution of Oceanography, brought deep learning to benthic image annotation, freeing researchers from labelling millions of sample points and allowing users to train their own science-ready models.
MERMAID, led by the Wildlife Conservation Society, built a field-to-dashboard workflow so reef teams can collect, validate, share, and analyze standardized survey data across fish, benthic, and bleaching methods; now with MERMAID AI, a single pre-trained image classifier provides an easy introduction for classifying coral reef images.
Today, we're excited to share what happens when those two platforms meet.
A partnership built on open-source technology
CoralNet and MERMAID share more than a mission to help advance coral reef conservation and science. We share an ethos: that the infrastructure underpinning coral reef science should be open, free, and built for the global community of researchers, managers, and practitioners who do the work.
Over the past two years, CoralNet and MERMAID have been working together to bring our tools into closer alignment. Not by collapsing them into one platform, but by making sure they work seamlessly together. Each is best at what it does:
CoralNet is the gold standard for scientific benthic image annotation. Its deep-learning models perform on par with human expert annotators, and users can choose their own label sets and train custom classifiers on their own image libraries.
MERMAID is purpose-built for end-to-end reef monitoring workflows, from offline field collection in remote sites in MERMAID Collect to analysis and data sharing on MERMAID Explore. Trained with CoralNet public imagery and using the CoralNet deep-learning feature extractor, MERMAID AI offers an easy-to-use image classifier for users to get started with photo quadrat analysis using a standard set of labels from the MERMAID benthic hierarchy.
First, we want to thank CoralNet for providing the public training data to build MERMAID’s pre-trained AI image classifier This scientifically validated training data was critical to developing MERMAID AI Image Classification (Beta), released in June 2025.
“While MERMAID harnesses the same underlying deep learning methods and feature extractor as CoralNet, the MERMAID classifier is trained with a unified set of labels of the most important taxa, allowing users to directly analyze their images without further training and to more readily compare results across studies, regions, and time.“ David Kriegman, co-founder of CoralNet.
Another question we kept hearing was: "How do I get my CoralNet outputs into MERMAID so I can analyze them alongside everything else I'm collecting?"
That question is what Easy PQT answers.
The new Easy Photo Quadrat (Easy PQT) app connects CoralNet data to MERMAID
Easy PQT, or ‘Easy Photo Quadrat’ is a free, web-based app in the MERMAID ecosystem that brings your benthic photo quadrat data from CoralNet directly into your MERMAID projects — no R, no scripts, no command line. Simply upload your CoralNet export file and start the import process into MERMAID.
In a few clicks, you can:
Upload your CoralNet CSV export into the Easy PQT app
Map your labels to MERMAID's standardized benthic attributes and growth forms
Resolve any flagged mismatches with built-in validation
Import your reshaped data straight into a project, ready for final review and submission in MERMAID Collect
Once your CoralNet data is in MERMAID, it sits alongside your fish, bleaching, habitat complexity, and other reef monitoring data in a single standardized format. From there, you can export your data from MERMAID Collect or Explore, analyze it using the MERMAID R package (mermaidr), visualize results through interactive chats in MERMAID Explore, and easily share findings with collaborators or contribute to global reef data efforts.
What this partnership means for monitoring teams using CoralNet and MERMAID
Keep using your own trained CoralNet models.
If you've invested time training a classifier on your region, your taxonomy label set, your image conditions, keep using those models in CoralNet! To combine these results with your other MERMAID surveys, export your results and upload them into your broader MERMAID workflow easily with EasyPQT.
One place for all your reef data.
MERMAID helps you integrate your photo quadrat data from any platform like CoralNet or ReefCloud with your bleaching, reef fish, benthic (PIT, LIT, or habitat complexity), and soon macroinvertebrate data.
Open, standardized, comparable.
Both CoralNet and MERMAID platforms are committed to open data and shared standards. Data flowing through this workflow can support local management decisions and contribute to global reef assessments. CoralNet and MERMAID are currently collaborating on developing a single shared ‘open data bucket’ to help improve future image classification models for coral reefs.
Free and open source.
Both tools are free for users and built with open source standards, not proprietary software. CoralNet has been supported by NSF, ,NOAA, and the Pacific Blue Foundation; MERMAID by a coalition of foundations and partners committed to conservation tech as public infrastructure.
How to get started
Try Easy PQT: https://datamermaid.shinyapps.io/easyPQT/
Read more about Easy PQT in our Reef Story product blog post
Read the documentation on how to use Easy PQT for CoralNet data
Read the documentation on how to use the MERMAID image classification (beta)
Learn more about CoralNet: https://coralnet.ucsd.edu/
You'll need a verified MERMAID account, and don’t forget to sign up for the MERMAID newsletter to receive updates about new features and upcoming events.
For teams that don't yet have their own trained CoralNet classifiers, this partnership also powers MERMAID AI, a benthic image classification classifier available directly inside MERMAID Collect. You can annotate photo quadrat images without setting up label sets or training your own models. As your program grows, you can graduate to more custom CoralNet classifiers and bring them back into MERMAID through Easy PQT. Same workflow, more power.
An open ecosystem for reef science
The reef monitoring community has spent decades building tools, standards, and datasets, often in parallel, sometimes redundantly. The promise of open-source conservation tech is that we don't have to keep working in silos in order to advance coral reef science and conservation.
We're grateful to David Kriegman and Stephen Chan from the CoralNet team for the collaboration, and to the funders — NSF, NOAA, Pacific Blue Foundation, and the philanthropic foundations behind MERMAID — who make this kind of open infrastructure possible. The coral reef community deserves tools that work together, and we'll keep building toward that.
If you have questions, need an onboarding session, or want to share how your team is using these tools, reach out at contact@datamermaid.org. We'd love to hear from you.