## 4 The M_HERACLES Toolbox
### Step 1: Using GIS to Aid the Segmentation of Large Area Point Clouds into Clusters of Objects
#### 4.1.2 Results and Discussion
For the St-Pierre dataset, quantitative values seem to validate the qualitative visual inspection of Figure 5 that the algorithm worked better than for Kasepuhan. In the statistical analysis presented in Table 2, only six of the most important heritage sites located within the neighborhood of the St-Pierre church were taken into account. In this reduced sample, the median unclassified percentage amounts to 6.64% (i.e., comparable to that of Kasepuhan), but the median precision attained a value of 98.86% and the median recall that of 93.69%, thus yielding a median F1 score of 93.90%. The best result was obtained for the Palais des Fetes (PLSFETES) building, with 100% precision (97.5% F1 score). The worst F1 score was obtained by the Palais de Justice (PLSUSTICE) building. This is due to a chunk of the point cloud of the said building which was visibly not segmented into the cluster. This unsegmented chunk corresponds to the scaffoldings on the building, erected due to renovations. The ALS points of the scaffoldings were so few that [PERSON] considered them as noise. Apart from this outlier, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Object**} & \multicolumn{2}{c}{**Point Number**} & \multicolumn{2}{c}{**Misclassed**} & \multirow{2}{*}{**True Positive**} & \multirow{2}{*}{**\%Unclassed**} & \multirow{2}{*}{**\%P**} & \multirow{2}{*}{**\%R**} & \multirow{2}{*}{**\%F1**} \\ \cline{2-2} \cline{5-8}  & **Manual** & & & & & & & & & \\ \hline BUILDINGS1 & 703 500 & 680 386 & 10 592 & 33 706 & 669 794 & 4.79 & 98.44 & 95.21 & 96.80 \\ \hline BUILDINGS2 & 643 350 & 633 897 & 6 630 & 16 083 & 627 267 & 2.50 & 98.95 & 97.50 & 98.22 \\ \hline BUILDINGS3 & 317 459 & 300 283 & 9 873 & 27 049 & 290 410 & 8.52 & 96.71 & 91.48 & 94.02 \\ \hline BUILDINGS4 & 58 532 & 60 838 & 8 296 & 5 990 & 52 542 & 10.23 & 86.36 & 89.77 & 88.03 \\ \hline BUILDINGS5 & 52 026 & 58 047 & 7 415 & 1 394 & 50 632 & 2.68 & 87.23 & 97.32 & 92.00 \\ \hline GATES1 & 101 196 & 95 754 & 4 017 & 9 459 & 91 737 & 9.35 & 95.80 & 90.65 & 93.16 \\ \hline GATES2 & 151 040 & 146 133 & 4 955 & 9 862 & 141 178 & 6.53 & 96.61 & 93.47 & 95.01 \\ \hline WALLS1 & 216 951 & 151 520 & 683 & 66 114 & 150 837 & 30.47 & 99.55 & 69.53 & 81.87 \\ \hline WALLS2 & 417 768 & 351 818 & 3 168 & 69 118 & 348 650 & 16.54 & 99.10 & 83.46 & 90.61 \\ \hline WALLS3 & 84 516 & 81 520 & 5 762 & 8 758 & 75 758 & 10.36 & 92.93 & 89.64 & 91.25 \\ \hline WALLS4 & 64 877 & 56 804 & 4 595 & 12 668 & 52 209 & 19.53 & 91.91 & 80.47 & 85.81 \\ \hline WALLS5 & 63 014 & 34 752 & 1 814 & 30 076 & 32 938 & 47.73 & 94.78 & 52.27 & 67.38 \\ \hline WALLS6 & 177 399 & 175 862 & 13 371 & 14 908 & 162 491 & 8.40 & 92.40 & 91.60 & 91.99 \\ \hline  & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: The quantitative analysis on the results of step 1 for Kasepuhan. In this table, three classes were taken into account (buildings, gates, and walls) with a total of 13 objects. %P is precision, %R is recall, and %F1 is the normalized F1 score. the most frequently encountered error seems to be related to the presence of vegetation or, analogous to the Kasepuhan data, the minor errors due to prior ground extraction. An interesting point to note in summarizing these results is the speed of the processing when compared to manual segmentation and labeling. The algorithm, while having several outliers (especially in the presence of important noise), generated good results. This is particularly true in the case of the St-Pierre dataset, where the urban density and particularly flat terrain generated very good results.