Earthworks such as earthen berms have been constructed across the western US since the late 1800s to mitigate erosion in landscapes where water is both the dominant driver of erosion and the limiting resource for biota. Berms alter hydrologic, geomorphic and ecologic processes by intercepting runoff and altering patterns of water availability in the landscape. Understanding site-specific changes in process dynamics requires accurate mapping of berm locations and knowledge of their condition. This paper presents an automated, object-based framework for identifying earthen berms from 1 m LiDAR-derived digital elevation models in the western US rangelands. Geomorphon, a computer vision tool, was used to classify landforms and identify berm-like landforms, including summits and ridges. Ten geomorphic and geometric attributes associated with each potential berm object were used to develop a machine-learning model for distinguishing berms from natural summits and ridges. The model was trained and applied to independent test sites to identify and map berms. The mapped berms were compared with manually identified reference berms for accuracy assessment. The identification results achieved 79% to 87% recall, 82% to 92% precision and 81% to 89% F-measure. We also explored the influence of training sample selection on model performance and conducted an analysis of attribute relative importance. The automated framework has the potential to be scaled up to larger areas in semi-arid environments.