Abstract Geomorphological maps provide essential information for the analyses of natural resources and hazards. Traditional methods often rely on photointerpretation, which can lead to subjective interpretations and interoperability issues in the final results. In contrast, automated methodologies utilizing digital elevation models (DEMs) offer a systematic approach for government agencies that need to map landforms of large territories as inputs for soil maps at various scales. However, existing automated methods based on some primary attributes such as slope, convexity and roughness may not fully capture the diversity of landform genesis. To address this gap, this study introduces a semiautomated method that extracts various landforms based on three primary attributes: slope, morphology and roughness. The effectiveness of the proposed method is evaluated by comparing it with a similar semiautomated segmentation technique based on three comparable variables, which define large regional‐scale units. The findings indicate that the proposed method better responds to the needs of detailed cartography in the following ways: (1) segments used improve the working scale (local and regional) representation; (2) roughness attributes facilitate the identification of geomorphological units; and (3) the DEM generation method enhances terrain representation. Overall, this study demonstrates the utility of semiautomated methods for generating detailed geomorphological maps, offering significant improvements over existing techniques. These advancements contribute to a better understanding of landscape dynamics and support various applications such as soil mapping.
Núñez et al. (Fri,) studied this question.