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Review on Deep Learning Methods, and their Implications for Mapping Geomorphic Landforms

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Author Affiliations

  • 1Department of Geology and Geography, West Virginia University, Morgantown, WV 26505, United States
  • 2Institute of Geology University of Azad Jammu and Kashmir, Muzaffarabad AJ&K, Pakistan

Int. Res. J. Earth Sci., Volume 14, Issue (2), Pages 8-17, August,25 (2026)

Abstract

The importance of accurate mapping of geomorphic and archeological features has arisen across the globe after the great success of deep learning techniques. Deep learning models are novel approaches utilized for the mapping of geomorphic landforms, and archeological features to improve accuracy, reduce time, provide automation, and produce high-resolution maps. Convolutional Neural Networks (CNNs) have proven to be one of the most promising techniques applied effectively for the mapping of geomorphic landforms. Spatial and semantic parameters of the CNNs and U-Net are reviewed in this work, and their implications for the accurate mapping of geomorphic landforms. Additionally, very recently, a series of studies have established that Convolutional Neural Networks (CNN) are very effective in pixel-level classification and aggregated landforms. This review presents a few examples where CNN has shown accurate results. CNN has measured an ice-wedge polygon in Alaska with a 91% accuracy rate, and the CNN used as an object-based image analysis in the northern Netherlands to map low-relief features. Furthermore, this study presents a comprehensive deep insight review into CNN and its utilization for accurate mapping of complex and transitional landforms since pixel and object-based techniques do not perform well in classifying complex and transitional landforms.

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