Turn an underwater image into seagrass data

A patch-based segmentation model designed for seagrass morphological class identification and habitat mapping. The model segments images into fixed-size patches (e.g., 280×280 pixels) and classifies each patch into foreground classes (various seagrass species) or background classes (such as substrate, coral, and macroalgae). Morphological seagrass coverage is quantified by calculating the percentage of patches classified into each class, enabling precise assessments of seagrass distribution and density.

A computer vision model designed to classify seagrass coverage from subtidal images into 3 cover categories : low seagrass cover (≥3 <10%), medium seagrass cover
(≥10 <30%), and high seagrass cover (≥30%). The percent coverage used as reference is from Seagrass-Watch percent cover standards on a 50x50cm quadrat. Please only use the model on images with at least 3% seagrass cover.

A computer vision model designed to identify the presence of specific seagrass species morphologies from subtidal images. The morphologies identified are : oval, strappy, ferny and cylindrical. Please only use the model on images with at least 3% seagrass cover.

A computer vision model designed identify the presence of seagrass from subtidal images. The percent coverage used as reference is from Seagrass-Watch percent cover standards on a 50x50cm quadrat. Please only use the model on images with at least 3% seagrass cover.