In the context of road survey with laser scanning technology, the identification of road structure elements and rutting using point clouds poses a significant challenge. In practice, it is not always feasible to perform scanning in accordance with all the established rules. This is due to the fact that point clouds may contain a significant amount of noise, and in certain areas, a lack of point density precludes the utilization of automatic classification algorithms. Furthermore, the complexity and lack of optimization of a number of algorithms often necessitates the limitation of the data researched and the significant consumption of available computer hardware resources. Consequently, the processing of the data is predominantly manual. Purpose: A method for identifying sloping/vertical structures and rutting should be developed. This method should be based on slope analysis between neighbouring cloud points. The focus should be on big data performance and the predictive value of hardware computing resource consumption. Methods: The present study employs point cloud transformation in conjunction with land classification according to the CSF algorithm. The method under consideration was implemented in the C++ programming language as an add-on to nanoCAD system of automated design (SAD) for working with point clouds in its environment. The GEOS open source geometry library was used as an auxiliary tool for the generation of contour. The validation of the proposed method was carried out by laser scanning inspection of St. Petersburg roads. Practical significance: The computational complexity of the method is dependent to a quadratic degree on the size of the point cloud, whilst remaining independent of its volume, a property that facilitates its application in the processing of lower quality point clouds. Results: A user is provided with a set of polygons (closed contours) for the required slope values, with the option of drawing polygons of a specified area.