Quantifying Intertidal Zone Species Using Semantic Segmentation

摘要

As anthropogenic impacts on marine ecosystems accelerates (e.g. warming, acidification, eutrophication, etc), it is essential to build robust datasets that establish biological baseline data and capture long-term trends in shifting species abundance and diversity. This data has traditionally been collected through continual revisits by skilled ecologists and taxonomists to long-term ecological monitoring sites. One novel technique developed by an intertidal ecology research group at California State University Channel Islands (CSUCI) builds 1m-wide photo-transects for the length of the tidal zone (20m from splash to low zone) at two sites on Santa Rosa Island. These photos are stitched together using software and offer high-resolution swaths of information at the island, taken twice a year. A machine learning technique, semantic segmentation, has been employed to automate the analysis of these large images, focusing first on a dominant algal species of rockweed Silvetia compressa. This automation will greatly reduce the time needed and human error involved in scoring and quantifying these transects. The study involves developing a convolutional neural network using transfer learning on a publicly available network.