Coral Image

Coral image analysis focuses on automating the classification and segmentation of underwater imagery to efficiently monitor coral reef health and biodiversity. Current research heavily utilizes deep learning, employing architectures like transformers and convolutional neural networks (CNNs), often enhanced by techniques such as superpixel segmentation and human-in-the-loop approaches to improve labeling efficiency and model accuracy. These advancements enable high-resolution 3D modeling and precise mapping of coral conditions, stressors, and species composition, providing crucial data for conservation efforts and reef management. The resulting improved efficiency and accuracy in analyzing large image datasets significantly aids in understanding coral reef ecosystems and their response to environmental changes.