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This study investigates the potential of automated geomorphological mapping using geomorphometric analysis and machine learning on the Yamal Peninsula, Russia. The research aims to classify landforms based solely on geomorphometric characteristics, bypassing traditional manual interpretation of aerial imagery and digital elevation models (DEMs). The study utilized a DEM of the Yamal Peninsula and a reference geomorphological map, including 10 distinct landform types. A total of 119 geomorphometric variables, inclu...

This study assesses the efficiency of various geomorphometric terrain characteristics and a number of categorical geological parameters in training statistical models to create geomorphological maps. The study area is the central sector of the western coast of Yamal Peninsula (S = 10 400 km2). The terrain is exceptionally flat and morphologically similar, but the genesis and age of the surfaces are diverse. The background surface type is marine terraces of various (Middle Pleistocene to Holocene) ages, subsequentl...