  Coral reefs are vital for marine biodiversity, coastal protection, and
supporting human livelihoods globally. However, they are increasingly
threatened by mass bleaching events, pollution, and unsustainable practices
with the advent of climate change. Monitoring the health of these ecosystems is
crucial for effective restoration and management. Current methods for creating
benthic composition maps often compromise between spatial coverage and
resolution. In this paper, we introduce BenthIQ, a multi-label semantic
segmentation network designed for high-precision classification of underwater
substrates, including live coral, algae, rock, and sand. Although commonly
deployed CNNs are limited in learning long-range semantic information,
transformer-based models have recently achieved state-of-the-art performance in
vision tasks such as object detection and image classification. We integrate
the hierarchical Swin Transformer as the backbone of a U-shaped encoder-decoder
architecture for local-global semantic feature learning. Using a real-world
case study in French Polynesia, we demonstrate that our approach outperforms
traditional CNN and attention-based models on pixel-wise classification of
shallow reef imagery.
