Fully convolutional neural networks applied to large-scale marine morphology mapping
Arosio, R.; Hobley, B.; Wheeler, A.J.; Sacchetti, F.; Conti, L.A.; Furey, T.; Lim, A. (2023). Fully convolutional neural networks applied to large-scale marine morphology mapping. Front. Mar. Sci. 10: 1228867. https://dx.doi.org/10.3389/fmars.2023.1228867
In: Frontiers in Marine Science. Frontiers Media: Lausanne. e-ISSN 2296-7745, meer
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| Auteurs | | Top | Dataset |
- Arosio, R.
- Hobley, B.
- Wheeler, A.J.
- Sacchetti, F.
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- Conti, L.A.
- Furey, T.
- Lim, A.
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| 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. |
| Dataset |
- Arosio, R.; Hobley, B.; Wheeler, A.; Sacchetti, F.; Conti, L.; Furey, T.; Lim, A.; University College Cork (UCC), Ireland; (2024): Morphological map of the Irish continental shelf created using Deep Learning., meer
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