Comparison of Machine Learning Results of Glacial Landform Classification of Datasets From Svalbard and Antarctica

In formerly glaciated regions, high resolution multibeam bathymetry is crucial to understand submarine glacial landforms and the processes that formed them. Using submarine glacial landforms to study past glacial dynamics contributes to the understanding of how glacial margins will change in the future. The increase in mapped seafloor coverage also creates an opportunity to develop and deploy automated models for recognizing and classifying glacial and glacimarine features. Datasets of glacial and glacimarine features were developed from manual interpretation of multibeam bathymetry from the deglaciated shelves of Svalbard and Antarctica. Features were assigned to one of 10 classes: crag and tails; flutes and drumlins; grounding zone wedges; mega-scale glacial lineations; channels; iceberg ploughmarks; large moraines; small moraine ridges; planes; and bedrock structures. Additional morphological analysis was performed for each feature in both datasets. For each sample, inputs to the model included bathymetry, slope, aspect, profile curvature, tangential curvature, and the rasterized outline of the feature of interest. We performed classification using six established Convolutional Neural Network (CNN) architectures, including ConvNeXtTiny, DenseNet201, EfficientNet, MobileNet, ResNet50, a baseline Simple CNN in addition to a Genetic Conditioned Convolutional Neural Network (GC-CNN). Three approaches were taken using the datasets: 1) Training separate models using the Svalbard and Antarctica datasets with testing only on the same dataset. 2) Training separate models using the Svalbard and Antarctica datasets and testing on the other dataset. 3) Training a combined model using all available data from both datasets and testing on a separate dataset from North Greenland. Our results show that morphological differences between features from different regions have a significant effect on machine learning model accuracy. Developing robust glacial landform classification models that can be applied to features from all regions require data that capture the variability of a particular feature class.