## 4 The M_HERACLES Toolbox
### Step 1: Using GIS to Aid the Segmentation of Large Area Point Clouds into Clusters of Objects
#### 4.1.3 Comparison with a Commercial Solution
When implemented on the St-Pierre dataset, M_HERACLES notably still performed better than Metashape as can be consulted in Figure 8. Metashape produced a very high precision rate; however, this must be understood with a caveat. Indeed, Metashape performed automatic segmentation on all buildings on the scene, whereas M_HERACLES only performed one on the \"public building\" class as dictated by the related shapefile. This distinction between public buildings and other buildings follows the official categorisation as set by the Strasbourg city geomatics service. In this regard, the results of Metashape were therefore manually segmented to include only the so-called public buildings, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**\%Precision**} & \multicolumn{2}{c}{**\%Recall**} & \multicolumn{2}{c}{**\%F1 Score**} \\ \cline{2-7}  & **Metashape** & **M\_HERACLES** & **Metashape** & **M\_HERACLES** & **Metashape** & **M\_HERACLES** \\ \hline Buildings & 51.44 & 95.40 & 73.49 & 77.14 & 60.52 & 85.30 \\ \hline Walls & 6.48 & 96.61 & 3.17 & 77.21 & 4.26 & 85.83 \\ \hline Trees & 92.15 & 88.23 & 85.12 & 74.80 & 88.50 & 80.96 \\ \hline
**Median** & **51.44** & **95.40** & **73.49** & **77.14** & **60.52** & **85.30** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative table showing the quantitative results of the classification for Kasepuhan using Metashape and M_HERACLES. Figure 6: Visual comparison of the point cloud segmentation process: **(a)** displays the manual classification used as reference, **(b)** the results of the Metashape automatic classification, and **(c)** the results from M_HERACLES. The color blue denotes the ’buildings’ class, orange the ‘walls’ class, and green the ‘trees’ class. Figure 7: Histogram representation of the classification performance for each class in the Kasepuhan dataset **(left)** and the median value of the principal quality parameters **(right)**. thus yielding a slight bias towards higher precision. However, as far as the recall value is concerned, M_HERACLES again outperformed Metashape. This is mainly due to the mis-classification rate of Metashape as can be seen in Figure 9. For example, in Figure 9a, much of the St-Pierre church dome and church towers were misclassified as high vegetation. This played a large role in explaining the low recall value for Metashape. Overall, in terms of F1 score, M_HERACLES also managed to outperform Metashape in this case of highly urban scene as opposed to Kasepuhan's more closed and isolated complex situation. As can be seen in this section, the proposed M_HERACLES algorithm managed to perform the classification of point clouds in the neighborhood scale fairly well. Comparison with Metashape also showed that our solution presented very promising and interesting results. Another advantage of M_HERACLES is the possibility to retrieve individual objects instead of a single cluster comprising all of the instances in the same class. This is useful when working with heritage sites, since, in many cases and for various reasons, the user may wish to acquire the point cloud of one or more specific buildings. Furthermore, the possibility to annotate these individual buildings with semantic information derived from the GIS shapefiles also presented an advantage for the developed algorithms. As far as the processing time is concerned, Metashape clocked a much faster time at around five minutes for both Kasepuhan and St-Pierre. This also shows the necessity for further optimization of M_HERACLES in terms of processing time, although the current time is already quite satisfactory considering the results obtained. Figure 8: Histogram representation of the quality parameters for the St-Pierre dataset comparing Metashape against M_HERACLES. Figure 9: Case of mis-classification in the results of the Metashape point cloud classification on the St-Pierre dataset.