Historic agricultural terraces are often poorly documented, especially where woodland obscures morphology. This paper presents an open source GeoAI workflow for semi automatic terrace mapping from LiDAR DEMs. By combining terrain, solar irradiance, soil erodibility and accessibility predictors, the extended Random Forest model improved detection, supporting heritage documentation and assessment.