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The accurate mapping of seafloor substrate types plays a major role in understanding the distribution of benthic marine communities and planning a sustainable exploitation of marine resources. Traditionally, this activity has relied on the efforts of marine geology experts, who accomplish it manually by examining information from acoustic data along with the available ground- truth samples. However, this approach is challenging and time-consuming. Hence, it is important to explore automatic methods to replace this manual process. In this study, we investigated the potential of deep learning (U-Net) for classifying the seabed as either "bedrock" or "non-bedrock" using bathymetry and/or backscatter data, acquired with multibeam echosounders (MBES). Slope and hillshade data, derived from the bathymetry, were also included in the experiment. Several U-Net models, taking as input either one of these datasets or a combination of them, were trained using an expert delineated map as reference. The analysis revealed that U-Net has the ability to map bedrock and non-bedrock areas reliably. On our test set, the models using either bathymetry or slope data showed the highest performance metrics and the best visual match with the reference map. We also observed that they often identified topographically rough features as bedrock, which were not interpreted as such by the human expert. While such discrepancy would typically be considered an error of the model, the scale of the expert annotations as well as the different methods used by the experts to manually generate maps must be considered when evaluating the predictions quality. While encouraging results were obtained here, further research is necessary to explore the potential of deep learning in mapping other seabed types and evaluating the models' generalization capabilities on similar datasets but different geographical locations. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Seabed classification of multibeam echosounder data into bedrock/non-bedrock using deep learning." The corrigendum addresses minor inaccuracies in the article, specifically regarding metrics values for multiple-input models and misclassification values in tables. The corrections have been made to the tables and corresponding confusion matrices. The authors apologize for the errors and state that they do not change the scientific conclusions of the article. [Extracted from the article]