Using three dimensional convolutional neural networks for denoising echosounder point cloud data. (March 2020) - Stephens, David
Abstract: It is estimated that over 80% of the world's oceans are unexplored and unmapped limiting our understanding of ocean systems. Due to data collection rates of modern survey technologies such as swathe multibeam echosounders (MBES) and initiatives such as Seabed 2030, there is ever-increasing increasing volume of seafloor data collected. These large data volumes present significant challenges around quality assurance and validation with current approaches often requiring manual input. The aim of this study is to test the efficacy of applying novel 3D Convolutional Neural Network models to the problem of removing noise from MBES point cloud data, with a view to increasing the automation of processing bathymetric data. The results reported from hold-out test sets show promising performance with a classification accuracy of 97% and kappa scores of 0.94 on voxelized point cloud data. Deploying a sufficiently trained model in a productionized processing pipeline could be transformational, reducing the manual intervention required to take raw MBES point cloud data to a bathymetric data product. Highlights: A convolutional neural network was trained to clean bathymetric 3D point cloud data. The trained model performed very well when tested on new survey data collected in a different area. A neural network model with skip layer connections achieved an accuracy of 97% and kappa score of 0.94 on the voxelized data.