The production of High-Definition (HD) maps has become an increasingly researched area in recent years, along with the rise of self-driving vehicles. One area is the automatic evaluation of road networks and their environment. This research presents a workflow for the evaluation of a point cloud dataset generated by a mobile mapping system. In the initial step of the procedure, a Shallow Neural Network (SNN) classification method is utilized to determine the segment of the point cloud corresponding to the path of the mapped area. Then, exploiting the GPS (Global Positioning System) time data of the points, the boundaries of the path are determined, which are used as a last step to carry out the OpenDRIVE structure. The presented workflow has been validated on real measurement data from the ZalaZONE Automotive Proving Ground. The results are convincing and justify the proposed methodology. The median cross-sectional deviation of the path achieved an accuracy of 5 cm, and the areas covered by the path showed a 97% similarity. The High Speed Handling course (one of the proving ground modules) has been used for the testing and validation of the semi-automatic HD map generation workflow. The generated HD maps and related models have been openly shared and also integrated into MATLAB software (since the 2026 release of MATLAB).