Classification of Glacial and Fluvioglacial Landforms by Convolutional Neural Networks Using a Digital Elevation Model

Abstract

The rise of artificial neural networks (ANNs) has revolutionized various fields of research, demonstrating their effectiveness in solving complex problems. However, there are still unexplored areas where the application of neural networks, particularly convolutional neural network (CNN) models, has yet to be explored. One area is where the application of ANNs is even expected is geomorphology. One of the tasks of geomorphology is the classification of landforms in a broad sense. Such classification requires a precise interpretation approach to create a homogeneous product, and this requires time and a uniform, consistent approach by the interpreter, which is not easy to achieve with manual operations. Classifications in geomorphology are mainly performed by manual or semiautomatic methods. The use of ANNs can complement and, in many areas, replace manual classification and reduce the time commitment of the interpreter, not least because of its repeatability and objectivity, which is a definite advantage in the case of geomorphological interpretation of vast areas. This article uses two popular CNN architectures, including VGG and residual neural network, to solve the problem of classifying glacial and fluvioglacial landforms based on a digital elevation model (DEM). The results of this article show that CNNs can produce high accuracy scores (up to 87% overall accuracy) for a ground-based dataset and are a suitable method for identifying glacial and fluvioglacial landforms using DEM data as input. The presented method provides an objective, reproducible, and fast tool for automatic geomorphological analysis of terrain imagery of vast areas.

Keywords

• Artificial neural networks (ANN)
• convolutional neural network (CNN)
• deep neural network (DNN)
• digital elevation model (DEM)
• glacial and fluvioglacial landforms
• light detection and ranging (LiDAR)