## 5 Results
### Analysis of the Automatic and Manual Point Classification  
Point classification represents a critical step in point cloud processing, significantly influencing their subsequent use, such as in creating digital terrain models, infrastructure analysis, or other applications. This analysis focuses on the comparison of automatic and manual point classification, emphasizing accuracy, efficiency, and the time demands of both approaches. The evaluation was based on classification classes, the number of correctly classified points, and the total time required for classification. The analysis was conducted in two different software environments--Leica Cyclone 3 DR and Trimble Realworks 12.2. These software tools enable point classification using distinct algorithms and workflows. This approach allowed us to identify the advantages and disadvantages of each method, with particular attention paid to factors such as differences in the number of points assigned to specific classification classes and the ability of the automatic classification to handle complex or specific scenarios. The results of this analysis provide a comprehensive overview of the capabilities and limitations of automatic and manual point classification methods in the context of point cloud processing. In the Leica Cyclone 3 DR software, the automatic classification generated five classification classes within approximately 10 min, with each layer automatically assigned by the software from its database. Following the automatic classification, a subsequent manual classification was performed to adjust incorrectly assigned points. During the manual classification process, these points were reassigned to the appropriate classification classes based on their actual characteristics and location. This process helped eliminate errors caused by automatic algorithms, especially in areas with a high level of detail or complex geometry. Manual classification also allowed for the verification of the accuracy of the automatic process and helped determine the extent to which automated procedures could be relied upon in point cloud processing. The most significant discrepancies between automatic and manual classification occurred in the Vegetation class, where more than 740,000 points were reclassified. Most of these points were originally placed in the Building and Ground classes. The software also struggled to detect unnecessary points, often incorrectly placing them in the _Building_ class. These points, due to poor reflection or other factors, were found floating freely in space. Throughout the processing, it became clear that the _Ground_ classification class was the most reliable and accurately defined. The total time for both automatic and manual classification with the Leica Cyclone 3 DR software was approximately 75 min. The same classification process was carried out with the Trimble Realworks software. Automatic classification with this software took only 3 min and divided the points into four classification classes. While the software processed the automatic classification in a very short time, the results reflect this limitation. Subsequently, incorrect points were corrected using manual classification, which took 110 min. In this case, the automatic classification was not as thorough as in the previous processing using Leica Cyclone 3 DR. This is evident from the number of points that needed to be reclassified into different classes. Processing with this software was more complex and time-consuming. According to the results, the worst-defined class was Remaining, into which many points from the Vegetation and _Building_ classes were incorrectly assigned. In many cases, these points should have been clearly placed in other relevant classification classes. The main advantage of this software is the short duration of the automatic classification process. However, its drawbacks include inaccurately classified points, complex and lengthy processing, and the need for extensive manual corrections.