AUTOMATIC RECOGNITION OF ALGAL BED AREAS BASED ON A LARGE-SCALE SEMANTIC SEGMENTATION MODEL FOR ESTIMATING CO₂ ABSORPTION BY BLUE CARBON

Abstract

 Measuring the CO₂ absorption of algal beds is one of the key issues for achieving carbon neutrality, but identifying the area of algal beds from UAV images requires a great deal of labor and experience. In this study, we propose a method for automatic recognition of algal beds using UAV images. The proposed method uses a model that enables semantic domain segmentation at the pixel level, and employs ViT-Adapter, one of the latest models. The advantage of this technique is that it effectively utilizes the knowledge of a trained large-scale model to recognize algal beds, and it can identify algal beds at the pixel level by adjusting the parameters of the model. In this study, we conducted learning using mask images of visually identified algal beds from aerial photographs, and further examined data expansion and other processing to adapt the learning to UAV images. The effectiveness of this method was verified through a demonstration using UAV images of the Erimo coast of Hokkaido.