STUDY ON RIVER MANAGEMENT METHOD USING UAV PHOTOGRAMETRY AND DEEP LEARNING

Abstract

Japan has many rivers in its land, and many river maintenance operations such as inspections and patrols are performed by visual inspection. Especially in frequent river patrols, personnel record anomalies and grasp the condition of river channels on site, while some of them can be made more efficient by combining UAV photogrammetry and image recognition by deep learning. In this study, we propose a method to classify regions such as sandbars and trees in river channels by applying Semantic Segmentation, which is one of the image recognition methods by deep learning, to ortho images obtained by UAV photogrammetry. The deep learning model was trained using aerial images with different shooting altitudes, and a highly accurate river region classification model could be obtained. We conducted an experiment to apply the model to ortho images with different ground sampling distance (GSD), and confirmed that general region classification can be performed even for ortho images with different GSD from training data.