## 3 Nature of Available Datasets  
The research described in this paper utilizes several datasets which are mainly heritage sites (Figure 2). The main datasets both involve multi-sensor and multi-scalar data. The multi-sensor aspect is due to the fact that the final point cloud is a result of the combination of several 3D sensors, in most cases photogrammetry (aerial and close range drone as well as close range terrestrial photos) and laser scanning (terrestrial, but also aerial LIDAR in the case of the St-Pierre dataset). The multi-scalar aspect is achieved by the recording of not only one particular building of interest, but also the heritage complex or the neighborhood around it. This is done in order to have a complete documentation of the heritage site within the context of its geographical location. The method of 3D data integration follows the existing workflow as described in [21; 46]. In order to create a common coordinate system, each of the available 3D data was georeferenced separately into the same geodetic coordinate system. In order to do this, topographical surveys were conducted in parallel to photogrammetry and laser scanning in both of the main datasets. Artificial targets were measured and thereafter integrated in the absolute orientation phase for photogrammetry and the georeferencing process of a TLS point cloud. The chosen coordinate system corresponds to the respective national mapping projection system of each entity, therefore ensuring that future projects may also be integrated easily. The multi-scalar aspect of the datasets is directly linked to the multi-sensor aspect. In general, both the main datasets were recorded in different scales in order to have different levels of details; i.e., the neighborhood scale level comprising the heritage complex was recorded using either drone photogrammetry or aerial LIDAR, the building scale level comprising individual heritage building exterior was recorded using TLS and close range photogrammetry, while the interior scale level as well as the architectural elements were scanned using TLS and in some particular cases also photogrammetry. In addition, two supporting datasets were also used to augment the research and serve as an objective experiment on the developed algorithm's performance. These datasets are generally specific in nature, and does not possess the multi-sensor and multi-scalar attribute of the two main datasets. They are, however, useful in order to give another perspective and test the capabilities of the algorithm. The two supporting datasets comprise of one dataset dedicated for the pillar detection algorithm (Section 4.2) and another for the beam detection part (Section 4.3). The main datasets used in this research are as follows:  
1. _Kasepuhan Royal Palace_, _Cirebon_, _Indonesia_ (**\"Kasepuhan\"**): This historic area dated to the 13 th century and includes several historical buildings within its 1200 m\({}^{2}\) brick-walled perimeters. A particular area of the dataset called Siti Inggil is of particular interest to the conservators as they represent the earliest architectural style in the palace compound. In this paper, the Siti Inggil area is used as a focal point, with one its pavilions (the Central Pavilion) used as a case study for the more detailed scale level. Heavy vegetation was present within Siti Inggil often overlapping with the buildings, which will provide a particular challenge for the algorithm described in Section 4.1. The site was digitized in May 2018 using a combination of TLS and photogrammetry (both terrestrial and drone), and was georeferenced to the Indonesian national projection system. Figure 2: The four datasets used in this research. For the main datasets, the algorithm started with the neighborhood to building segmentation and followed by larger-scale segmentation of an example building (point cloud in the subset) into architectural elements.