Repozytorium publikacji - Politechnika Gdańska

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 aremainly performed bymanual 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, includingVGGand residual neural network, to solve the problem of classifying glacial and fluvioglacial andforms 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.

Autorzy

• Paweł Nadachowski,
• dr hab. inż. Zbigniew Łubniewski link otwiera się w nowej karcie ,
• Aleksandra Malecha-Łysakowska,
• mgr Karolina Trzcińska,
• dr Radosław Wróblewski,
• Jarosław Tęgowski