🪸 Coral-CAT: Semi-Automatic Coral Color Analysis Tool

Streamlining Coral Health Assessment Through Automated Color Analysis

KVL Staff on Project

Didier Barradas Bautista
didier.barradasbautista@kaust.edu.sa
Building 1, Level 0, Office 0125

KAUST PI on Project

Raquel S. Peixoto
raquel.peixoto@kaust.edu.sa

KAUST RS on Project

Neus Garcias-Bonet
neus.garciasbonet@kaust.edu.sa

Research Breakthrough

Coral health monitoring traditionally relies on manual color scoring—a time-consuming and subjective process. Coral-CAT introduces a semi-automatic pipeline that extracts coral color from images and assigns standardized color codes using reference charts.

  • 📊 Validated on 68 coral fragment images and underwater colony photos, Coral-CAT showed strong correlation with human assessments (R² = 0.93).
  • 🎯 Features include batch processing, OCR-based color chart extraction, and segmentation using CoralSCOP.
  • 🔬 Developed collaboratively by KAUST’s Biological and Environmental Sciences Division and Visualization Core Lab.

🔬 Research Visualization The graphical abstract below illustrates Coral-CAT’s workflow for automated coral color analysis. The paper entitled *“Coral-CAT: A Semi-Automatic Coral Color Analysis Tool”* is available for download and review. here.

🎯 KVL's Contribution: KVL's visualization scientist Didier Barradas-Bautista contributed significantly to the development and validation of Coral-CAT through:

  • Design and implementation of the image analysis pipeline
  • Integration of OCR and segmentation models for color chart and coral isolation
  • Development of batch processing and visualization features
  • Performance benchmarking against manual scoring methods
  • Deployment of the web-based application for community use
,