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Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAractérisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6–60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice. [ABSTRACT FROM AUTHOR]