Population growth frequently causes expansion of urban populations and associated transportation facilities onto formerly uninhabited slopes, where naturally occurring and constructed rock faces present potential hazards from rock fall and rock face instability. Rock fall management and mitigation programs are widely used to ensure public safety, to provide efficient rock face maintenance, and to proactively address potential rock fall hazards. These programs require comprehensive geologic characterization of individual rock slopes. New ways of collecting data are urgently required to accelerate field data acquisition, reduce identification difficulties, eliminate safety constraints, and remove human bias and subjectivity in the data. Exposed rock surfaces can be scanned quickly and accurately using a ground-based laser scanner, resulting in a collection of millions of discrete points each having a 3D value relative to the scanner's position. These "point clouds" have high spatial resolution, with adjacent points often only a centimeter or two apart. Relatively small surface features and discontinuities are readily identified. Algorithms have been developed to support semi-automated processes for orienting the point cloud, creating a polygonal surface model from the point cloud, determining rock discontinuity information, and defining block size distributions.