## 5 Results
### Analysis of the Automatic and Manual Point Classification
Figure 9 illustrates the building from various perspectives before classification. It then shows the results of automatic and manual classification, highlighting selected objects such as a tree, building, car, and traffic sign. The figure also demonstrates shortcomings and differences in the form of incorrectly classified pointson these objects. Table 6 presents the classification classes and their changes, including increases or decreases in the number of points. Quantitative evaluation of the classification results was conducted using standard metrics: precision, recall, and F-score (Table 7). This evaluation was performed in two software environments--Leica Cyclone 3 DR and Trimble Realworks 12.2--across the classification classes of ground, vegetation, and buildings. The comparison of automated point cloud classification between Leica Cyclone 3 DR and Trimble Realworks reveals significant differences in their performance. For ground point classification, Leica Cyclone 3 DR achieved high precision (0.984) and sensitivity (0.962), resulting in an excellent F-score of 0.973. These values suggest that the Leica Cyclone 3 DR algorithm can reliably identify ground points with minimal misclassifications. The number of false negatives (FN = 241,423) was relatively low, highlighting the algorithm's ability to accurately recognize most points \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Software**} & \multirow{3}{*}{\begin{tabular}{c} **Automatic** \\ **Classification** \\ **Classes** \\ \end{tabular} } & \begin{tabular}{c} **Number of** \\ **Automatic** \\ **Classification** \\ **Class Points** \\ **[Points]** \\ \end{tabular} & \multirow{3}{*}{\begin{tabular}{c} **Number of** \\ **Automatic +** \\ **Manual** \\ **Classification** \\ **Class Points** \\ **[Points]** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **The** \\ **Difference** \\ **Between** \\ **Point** \\ **Clouds** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Classification Time** \\ **Point** \\ **Clouds** \\ \end{tabular} } \\ \hline \multirow{3}{*}{\begin{tabular}{c} **Leica** \\ **Cyclone** \\ **3 DR** \\ \end{tabular} } & Ground & 6,197,508 & Ground & 6,345,060 & \(\dagger\) 90,434 \(\dagger\) & Automatic \\  & \begin{tabular}{c} Medium \\ vegetation \\ **3 DR** \\ \end{tabular} & 5 & \begin{tabular}{c} 5,138,343 \\ Building \\ **3 DR** \\ \end{tabular} & \begin{tabular}{c} Vegetation \\ 41,697,235 \\ **422,548** \\ **Unnecessary** \\ **Points** \\ \end{tabular} & 4,617,062 & \(\dagger\) 351,727 \(\dagger\) & Automatic + manual \\  & \begin{tabular}{c} Ground \\ vegetation \\ **3 DR** \\ \end{tabular} & \begin{tabular}{c} 5,452,391 \\ **High vegetation** \\ **3051,381** \\ **Building** \\ **300,468,554** \\ **Remaining** \\ \end{tabular} & \begin{tabular}{c} Ground \\ 3,051,381 \\ **Building** \\ **300,468,554** \\ **Remaining** \\ \end{tabular} & \
\begin{tabular}{c} 6,058,964 \\ **Vegetation** \\ **Vegetation** \\ **40,468,554** \\ **Remaining** \\ \end{tabular} &
\begin{tabular}{c} 6,058,964 \\ **Vegetation** \\ **300,467** \\ **Automatic + manual \\ classification** \\ **110 min** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of the automatic and manual classification on individual objects. Figure 9: Viewing automatic and manual classification on an individual object. Legend: brown—Ground, green—Vegetation class, red—Buildings class, blue—Hardscape class, grey—Unclassified class. in this class. In medium vegetation, Leica Cyclone 3 DR demonstrated slightly lower precision (0.854), linked to a higher number of false positives (FP = 750,427). However, sensitivity remained very high (0.988), indicating that most vegetation points were correctly identified. The resulting F-score (0.916) reflects a good balance between precision and sensitivity. For building classification, Leica Cyclone 3 DR showed exceptional performance, with a precision of 0.995, a sensitivity of 0.996, and an F-score of 0.995.