## 1 Introduction  
For the generation of a digital terrain model (DTM), it is necessary to identify the terrain points on the bare earth surface. Thus, all non-terrain-points such as vegetation, buildings and other constructions above the ground must be removed, which is referred to as filtering in this context [1]. There are different approaches for filtering laser scanning data. Some examples of basic algorithms, which have been modified in many ways, are mathematical morphology [2, 3], interpolation-based [4], and triangulated irregular network (TIN)-densification. By using geometrical values such as distances and angles as filter criteria [5], algorithms progressively densify the digital terrain model (DTM) to approximate the bare earth [6, 7]. These common ground modelling methods have been manly developed for airborne laser scanning (ALS) data and are not optimally suited for ground level determination from terrestrial laser scanning (TLS) data. The intrinsic point cloud densities, scanning distances, the distribution of noise points and the backscattering geometry of TLS data vary significantly when compared to ALS data. As a terrestrial laser scanner is located within the target area, complete occlusion to some viewing directions is highly probable. The spatial distribution of TLS points are heavily concentrated around the immediate vicinity of the scanner, resulting in excessively high point cloud densities near the scanner that become detrimental for the point cloud processing [8]. Further, many ground filtering techniques are limited in application within challenging topography and experience difficulty coping with some objects such as short vegetation, steep slopes, and so forth. Lastly, due to the large size of point cloud data, operations such as data traversing, multiple iterations, and neighbour searching significantly affect the computation efficiency [9]. Some methods have been developed especial for TLS data to segment into ground and non-ground points. [PERSON] and [PERSON] [10] present a supervised classification method using multi-scale dimensionality analysis based on the geometric characteristics of the scene. Combining this information from different scales can build signatures of the scene at each point. This signature can then be used to discriminate vegetation from soil for example. [PERSON] et al. [11] use the information of a multireturn TLS for vegetation mapping. First, the thresholds of the return number and the normalized amplitude of intensity are set to determine possible ground points. Subsequently, a progressive morphological filter is adopted for generating a DTM and digital surface model (DSM) where the maximum and minimum \(Z\) values are used as criteria to apply a dilation and erosion operator. [PERSON] et al. [12] separate ground and non-ground of TLS data by using the RGB value. [PERSON] et al. [13] developed a scan line continuousness segmentation well suited for trunk detection. The approach is based on the continuity property of the object surface, planar distance and segmentation in horizontal and vertical directions. The one-dimensional segmentations were first performed independently and then combined to groups with similar distances to the scanner. [PERSON] and [PERSON] [9] present a fast ground filtering method for TLS data via a Scanline Density Analysis. The process first separates the ground candidates, density features, and unidentified points based on an analysis of point density within each scanline. Then, a region growth using the scan pattern is performed to cluster the ground candidates and further refine the ground points (clusters). The method of [PERSON] [14] and [PERSON] et al. [8] uses a priori information of the TLS configurations and scanner-centred coordinates to determine the ground level. Integrating these coordinates in the filtering process is an important additional filter parameter. Some of these methods need additional data that are not always available or are made for a special purpose such as tree location mapping.