Terraces, found worldwide, demonstrate the interaction between humans and the environment, shaping landscapes, reducing slope, enabling agriculture, and mitigating erosion. Abandonment leads to negative consequences at the productive, economic, ecological, and landscape levels, with biodiversity loss and increased hydrogeological risk. In this view the semi-automated recognition of human terraces is crucial for land management, especially in areas with high historical and landscape value. Therefore, developing efficient and smart mapping methodologies is fundamental for protection, redevelopment, and valorization of such landscape. Geomorphological analysis tools were tested, with the r.geomorphon and r.param.scale modules proving most effective in delineating terraces. These methods, based on pattern recognition and local topography analysis, are well-suited to the identification of artificial structures like terraces. The r.geomorphon method generates geomorphometric maps from DEMs with a high number of cells, while r.param.scale helps to identify the characteristic of landforms, which can be useful in distinguishing terraces from other territorial features. The results of the research can be useful for terrace identification in other areas thus, supporting landscape conservation and valorization planning. Terraced landscapes preserve traditional agricultural practices, quality products, and biodiversity. The proposed methodology can came in handy in areas lacking terrace mapping that are abandoned or heavily vegetated.