Comparison of Deep Learning Approaches in Classification of Glacial Landforms

Paweł Nadachowski; Zbigniew Łubniewski; Karolina Trzcińska; Jarosław Tęgowski

doi:10.24425/ijet.2024.152066

Comparison of Deep Learning Approaches in Classification of Glacial Landforms

Abstrakt

Glacial landforms, created by the continuous movements of glaciers over millennia, are crucial topics in geomorphological research. Their systematic analysis affords invaluable insights into past climatic oscillations and augments understanding of long-term climate change dynamics. The classification of these types of terrain traditionally depends on labor-intensive manual or semi-automated methods. However, the emergence of automated techniques driven by deep learning and neural networks holds promise for enhancing efficiency of terrain classification workflows. This study evaluated the effectiveness of Convolutional Neural Network (CNN) architectures, particularly Residual Neural Network (ResNet) and VGG in comparison with Vision Transformer (ViT) architecture in the glacial landform classification task. By using preprocessed input data from Digital Elevation Model (DEM) which covers regions such as the Lubawa Upland and Gardno-Leba Plain in Poland, as well as the Elise Glacier in Svalbard, Norway, comprehensive assessments of those methods were conducted. The final results highlight the unique ability of deep learning methods to accurately classify glacial landforms. Classification process presented in this study can be the efficient, repeatable and fast solution for automatic terrain classification.

Cytowania

0

CrossRef
0

Web of Science
0

Scopus

Autorzy (4)

Paweł Nadachowski
Zbigniew Łubniewski dr hab. inż.
Karolina Trzcińska mgr
Jarosław Tęgowski

Cytuj jako

Pełna treść

pełna treść publikacji nie jest dostępna w portalu

pełna treść artykułu zobacz w serwisie zewnętrznym otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

International Journal of Electronics and Telecommunications nr 70, strony 823 - 829,
ISSN: 2081-8491

Język:

angielski

Rok wydania:

2024

Opis bibliograficzny:

Nadachowski P., Łubniewski Z., Trzcińska K., Tęgowski J.: Comparison of Deep Learning Approaches in Classification of Glacial Landforms// International Journal of Electronics and Telecommunications -,iss. vol. 70, no. 4 (2024), s.823-829

DOI: