## 1 Introduction  
High resolution LiDAR surveys have been used as basic data for the inventory of both natural and man-made resources since the 1990s [1]. The obtained point clouds (PCs) are an accurate, unscaled representation of the scanned space [2]. The way they are used and their usefulness depend on the source, the data [3, 4] and their resolution [1]. Point clouds that are obtained from airborne laser scanning (ALS) with densities ranging from a few to several dozen points per square meter (pt/m\({}^{2}\)) and with an accuracy of about 0.3-0.1 m [5] are normally used to inventory huge spaces as well as high-volume resources such as open pit mines, post-extraction heaps [6], railway or road embankments [7; 8; 9], landslide investigation [10] and various land cover features [11]. On the contrary, data from terrestrial laser scanning (TLS) with densities that range from several dozen to several hundred or even several thousand pt/m\({}^{2}\) are used to inventory larger-scale resources such as quarries, gravel pits, sandpits [12] and areas that do not exceed 1 km\({}^{2}\)[3, 4]. High resolution TLS data can be used to estimate the volume of mineral deposits in order to determine the quantitative potential. The estimation of the resources' cubic capacity (e.g., gravel or sand deposits) not only allows for assessing the economic viability of their exploitation or selecting the appropriate type of transport, but also planning the best course of action aimed at the revitalization of post-mining areas. Repeat surveys are conducted with the intent of controlling for changes at the assumed time intervals in long-term investments as well as natural processes of geomorphic character such as erosion or deposition, which are extended in time. Such data sets, processed into digital elevation models (DEMs), make it possible to get accurate estimations of both changes in the altitude of the topographic surface and the volume in the assumed surface area where estimating the latter occurs indirectly. One of the methods of DEM differentiation at various time intervals was developed by [PERSON] [13], which is the DEM of difference (DoD). Comparative studies using the DoD were used for the volumetric estimation of erosion and deposition as well as changes in the position of the topographic surface, e.g., [14; 15; 16; 17; 18]. In this method, the accuracy of estimating changes in the surface and thus the loss or increase in the resource depends on the accuracy of extracting from the raw point cloud only those points that represent the ground point [19]. On the one hand, generally available ALS data are usually provided as classified in accordance with the American Society for Photogrammetry and Remote Sensing (ASPRS) Standard LIDAR Point Classes [20] among which ground points are separated into class 2: Ground [20]. On the other hand, TLS data are usually obtained as raw and unclassified. It is in the case of the aforementioned data that the extraction of ground points from the entire cloud still poses a challenge and remains a process with a high risk of error [21]. The highest accuracy of classification is achieved by manual point cloud cleaning of all off-ground objects. However, this is an extremely time-consuming and tedious process, which can account for up to 80% of the time in the DEM development process [22; 23]. Despite the widespread use of LiDAR data for DEM creation and the development of software that supports mass point cloud (PC) data processing, the classification process has proved to be extremely difficult to automate [24]. Large data sets of areas with varied land cover have proven to be a particular problem. Recently published works provide information on new generally available algorithms classifying PCs, which enable the separation of ground points, e.g., developed for the American Forestry Service (Fusion) [25], the Simple Morphological Filter [26], the Multilevel Adaptive Filter (MAF) [27] and the Cloth Simulation Filter (CSF) [28].