## 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  
The GIS shapefile data shown in Figure 4 were used to aid the segmentation process. In the case of the St-Pierre dataset, the shapefile was acquired through the open data framework of the Strasbourg municipal council, the \"_Referentiel topographiques simplific_\" (RTS) or simplified topographic reference. The RTS shapefile data consist of several classes, but, for the purposes of this study, only the \"public building\" class will be addressed. For the Kasepuhan dataset, however, no prior shapefile was available for the site. The shapefiles of several object classes were therefore generated via digitization of the orthophoto of the site, which was also made available during the acquisition mission. The digitization was made to be not very precise on purpose, in order to test the robustness of the developed function. The original Kasepuhan dataset consisted of 10.4 million points, and were segmented into four classes (buildings, walls, gates, and the ground) and 13 different annotated objects in about 10 min. In the case of the St-Pierre dataset, the algorithm was visually more successful in segmenting the ALSpoint cloud into the public building class and annotating them. The St-Pierre dataset consisted of 5.9 million points and was segmented into 17 objects of one class in about 7 min. Here, again, the visually higher success of the St-Pierre dataset may be due to the better CSF results, thus giving a cleaner result than Kasepuhan. Figure 5 shows the results for six of the most important and thus interesting heritage buildings within the class. Table 1 presents a quantitative analysis on the obtained results for the two datasets. In Table 1, the number of segmented points is used as a parameter of segmentation quality. The \"overclassed\" column indicates classified points which present a Type I error (false positives), while the \"unclassed\" column denotes the number of points showing a Type II error (false negatives). Four statistical values  
Figure 4: The GIS shapefile data used to help the segmentation process. On the left, three shapefiles were available for the Kasepuhan dataset while, on the right, only one shapefile entity was used for the St-Pierre dataset. Figure 5: Results of the automatic segmentation and annotation (step \(1\) of M_HERACLES) for the St-Pierre dataset. Only six of the most important buildings are shown in this figure, of which BatimentsPublic3 represents the St-Pierre church which will be further processed. Note that only the aerial LIDAR data are shown. were used to assess the quality of the segmentation: percentage of unclassified points (i.e., Type II error rate), precision, recall, and the F1 normalized score. On the Kasepuhan data, overall, the unclassified rate from all 13 objects yielded an average value of 13.66% and a median value of 6.53%. Meanwhile, the precision median is also quite high at 95.80% with a lower median recall value of 90.65%, thus giving a median F1 score of 91.99%. While this value is seen to be good enough, the quality for each class differs. The buildings and walls class fared the best, with an average F1 score of 93.81% and 94.09%, respectively, although the score for the walls class may be biased since it only consists of two objects. As far as the buildings are concerned, BUILDINGS4 presented the largest error, which is caused by the remaining unfiltered ground around the structure. The walls class presented the worst results with an average F1 score of 84.82%. The WALLS class presents poorer recall value, which may be due to the significant presence of noises. This is particularly true for WALLS5 where the presence of large flower pots rendered the point cloud very noisy. It is also interesting to note that the quality of the ground extraction algorithm played an important role in the results; indeed, worse results were obtained for smaller objects on which the applied ground extraction function fared worse in distinguishing between the object and the ground.