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Fully convolutional neural networks applied to large-scale marine morphology mapping

Authors :
Riccardo Arosio
Brandon Hobley
Andrew J. Wheeler
Fabio Sacchetti
Luis A. Conti
Thomas Furey
Aaron Lim
Source :
Frontiers in Marine Science, Vol 10 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

In this study we applied for the first time Fully Convolutional Neural Networks (FCNNs) to a marine bathymetric dataset to derive morphological classes over the entire Irish continental shelf. FCNNs are a set of algorithms within Deep Learning that produce pixel-wise classifications in order to create semantically segmented maps. While they have been extensively utilised on imagery for ecological mapping, their application on elevation data is still limited, especially in the marine geomorphology realm. We employed a high-resolution bathymetric dataset to create a set of normalised derivatives commonly utilised in seabed morphology and habitat mapping that include three bathymetric position indexes (BPIs), the vector ruggedness measurement (VRM), the aspect functions and three types of hillshades. The class domains cover ten or twelve semantically distinct surface textures and submarine landforms present on the shelf, with our definitions aiming for simplicity, prevalence and distinctiveness. Sets of 50 or 100 labelled samples for each class were used to train several U-Net architectures with ResNet-50 and VGG-13 encoders. Our results show a maximum model precision of 0.84 and recall of 0.85, with some classes reaching as high as 0.99 in both. A simple majority (modal) voting combining the ten best models produced an excellent map with overall F1 score of 0.96 and class precisions and recalls superior to 0.87. For target classes exhibiting high recall (proportion of positives identified), models also show high precision (proportion of correct identifications) in predictions which confirms that the underlying class boundary has been learnt. Derivative choice plays an important part in the performance of the networks, with hillshades combined with bathymetry providing the best results and aspect functions and VRM leading to an overall deterioration of prediction accuracies. The results show that FCNNs can be successfully applied to the seabed for a morphological exploration of the dataset and as a baseline for more in-depth habitat mapping studies. For example, prediction of semantically distinct classes as “submarine dune” and “bedrock outcrop” can be precise and reliable. Nonetheless, at present state FCNNs are not suitable for tasks that require more refined geomorphological classifications, as for the recognition of detailed morphogenetic processes.

Details

Language :
English
ISSN :
22967745
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Marine Science
Publication Type :
Academic Journal
Accession number :
edsdoj.2cd8a8ad18334c848ea064d34f122e2d
Document Type :
article
Full Text :
https://doi.org/10.3389/fmars.2023.1228867