## Materials and Methods
### Model-Driven Modelling  
Model-driven structure modelling was carried out using several software programs, such as 3D Basemaps Solution implemented in ESRI's Arcgis Pro (ESRI, Redlands, CA, USA) [44], Whitebox Tools (Whitebox Geospatial Inc., Guelph, Ontario, Canada) [48], OpenStreetMap (OpenStreetMap Foundation, Cambridge, England, UK), Autodesk Meshmixer (Autodesk Inc., San Rafael, CA, USA), and MeshLab. The quality of the 3D models is related to the density of the point cloud and the accuracy of the segmentation of the building footprint. In order to obtain satisfactory results, ESRI's 3D Basemaps Solution requires an input point cloud with a point spacing of 1 metre or less; a recommended value for the spacing between points is 0.3 m. The point cloud taken into consideration respects the minimum value required and also the value recommended by ESRI. To create a 3D model of the buildings, we need four input data: DTM, DSM, normalized Digital Surface Model, and nDSM and building footprint. Figure 12 shows the building footprint segmentation based on the nadir images of the implemented method. To simplify the process of constructing the 3D model and, at the same time, increase the readability of the paper, only one of the six segmentation methods tested is shown in Figure 12. Since the geometry of the analysed structure is quite complex, the roofs were merged and, consequently, the final model could be textured by using nadir and oblique images. In general, DTM, DSM, and nDSM can be derived even from external sources (e.g., the building footprint may be derived from a regional database). In this case, these geomatics data were automatically generated in 3D Basemaps starting from the point cloud (Figure 13) using two tasks: \"Extract Elevation from LAS Dataset\" (DTM, DSM, and nDSM) and \"Extract building footprints\" (building footprint). Figure 12: Model-driven modelling pipeline. The roof of the structure was very complex to be contained in the 3D Basemaps database; for this reason, to model the roof, it was necessary to apply the \"divide and conquer\" strategy. In this way, the entire roof was divided into a set of elementary roofs that could be recognized and modelled by 3D Basemaps. In practice, we segmented the building footprint (Figure 13d) in order to separate contiguous buildings. 3D Basemaps was assigned a roof in the database to each individual building extracted from the segmented structure footprint. In order to test how the segmentation modality affects the 3D model of the structure, we segmented the footprint in different modes:  
1. Segmentation based on DSM;
2. Segmentation based on the point cloud;
3. Automatic segmentation based on nadir images;
4. Segmented building footprint from OpenStreetMap--OSM;
5. Segmentation based on the OSM features;
6. Interactive segmentation based on nadir images. A 3D model was automatically extracted from each segmented footprint, DTM, DSM, and nDSM (3D Basemaps: Create 3D Buildings). The models were numbered from 1 to 6 corresponding to the type of segmentation (Figure 14). Figure 14: Cont. Figure 13: Geomatics data visualisation in ArcGISPro software: point cloud (**a**), DTM (**b**), DSM (**c**), footprint of the structure (**d**). The models were analysed to determine the quality of the 3D model in relation to the segmented footprint building. In particular, in ArcGIS Pro software, the value of \"RMSE\" was verified, calculated between the model and DSM, and contained in the field of attribute tables of the 3D model generated in this environment. Figure 14: Segmented building footprints and extracted models: segmentation based on DSM 1 (**a**), model 1 (**b**), point cloud segmentation 2 (**c**), model 2 (**d**), automatic image-based segmentation 3 (**e**), model 3 (**f**), OSM building footprint 4 (**g**), model 4 (**h**), map-based OSM segmentation 5 (**i**), model 5 (**j**), interactive image-based segmentation 6 (**k**), model 6 (**l**). In Cloud Compare software, the cloud-to-mesh distance (C2M) was performed.