Deep Learning-Based Coral Reef Segmentation: A Satellite Imagery Study of the Trincomalee District

Abstract

Coral reefs, vital yet endangered ecosystems, face rising threats from climate change and humans. Accurate assessment of coral reef health is essential for early detection of ecosystem decline and effective conservation planning. We present a deep learning framework that utilizes satellite imagery and rugosity index analysis to automate coral reef segmentation in the Trincomalee District, Sri Lanka. High-resolution Google Earth Pro images were processed to compute rugosity index values, distinguishing coral reefs and enabling the creation of a new training dataset. A U-Net model, trained on 300 annotated images augmented to 1,200 samples, achieved robust segmentation of coral reefs (Dice coefficient = 0.86, specificity = 0.98). Our case study found differences in reef rugosity and extent across sites exposed to varying hydrodynamic conditions, emphasizing the interplay between hydraulic forces and reef health. Applying the model to satellite images allowed us to quantify declines in reef area and structural complexity in response to increased sediment loads and wave exposure. Furthermore, as no public coral reef training datasets exist for Sri Lanka to enable automated analysis, we prepared a new dataset. These insights aid in identifying vulnerable zones and support conservation, targeted hydraulic management, and future health assessments of Trincomalee’s coral reefs.