## 5 Conclusions and Future Work  
This paper presents a toolbox of functions dedicated to point cloud processing in the context of heritage objects. The main driving cause of the development of this toolbox is to address the increasingly multi-sensor and multi-scalar nature of heritage documentation. The presented M_HERACLES toolbox enables the user to automate some of the bottlenecks in the 3D processing pipeline of a multi-scalar point cloud, especially in the segmentation of individual buildings from the point cloud of the neighborhood and the detection of two classes of architectural elements. This was done to reduce human intervention and thus human error. Results for the three presented particular functions look promising. The historical complex to historical building segmentation and classification elaborated in Section 4.1 managed to perform the task correctly, all while retaining the classification according to the input shapefile. The algorithm presented in the paper also managed to automatically annotate the GIS attributes into the segmented clusters in an acceptable processing time, which may prove very useful not only for heritage purposes but also for general mapping purposes. Several caveats still exist, however. As has been previously discussed and detailed in [56], the correct segmentation ordering is important in order to get good results, especially in cases where vertical stacks are present (e.g., trees and building roofs). As a rule of thumb, lower entities (ground, low vegetations, etc.) should be segmented first before taller entities (e.g., buildings, tall vegetation, etc.). As has been previously explained, the ground extraction at the beginning of the algorithm is also an important factor influencing the final product. That being said, the attained median F1 scores of 91.99% for Kasepuhan and 93.90% for St-Pierre are very encouraging. Comparison with an external solution (Agisoft Metashape) was also performed in this section. Results on both the Kasepuhan and St-Pierre datasets showed that M_HERACLES managed to outperform Metashape as regards the precision and quality of the classification process. However, it should be noted that Metashape uses a machine learning approach in which the availability of training data are paramount. We are quite confident that machine learning solutions will become better as time goes; indeed, one of the main objectives of M_HERACLES is not to confront its performance directly with machine learning solutions, but rather to complement it via e.g., automation of training data creation. In Section 4.2, tests on two additional datasets in addition to the results described in [67] showed that the algorithm is useful in performing fast segmentation and classification for structural supports. However, as has been shown in the results, the algorithm, while fast and easy to use, remains prone to noise and deviations from the hard-coded geometrical rules. This is evidenced by the stark contrast between the three datasets, whereas Kasepuhan presented cases with higher precision but lower recall (suggesting underclassification), the Valentino showing higher recall but lower precision (suggesting overclassification) and the St-Pierre presenting somewhat of a mix between the two cases. These differences were caused by deviations from the general rule; the Kasepuhan interior ceiling does not correspond to the altitude of the roofs on the exterior, while Valentino's non-planar ceiling caused the error. The St-Pierre case showed that the proneness of the algorithm against noises. However,