# Massive Data Capture Approach for Modeling Existing Building Stocks
## 5 Discussion  
The novel approach presented in Section 2 was successfully validated by applying it for the first time to the case study and obtaining a final model of Bellivitge school, the reference building of the BCN.C2 cluster [71]. This model can be used in the following steps of the project to carry out energy, lighting, fire safety, and mechanical simulations, among others [42]. The inclusion of the surroundings in this model means that more accurate simulations can be performed that consider the urban context. It also facilitates the inclusion of these data elements into future research or public institution reports, which enables broader meta-analyses and comparative evaluations of modeling performance. This represents an advancement compared to most previous projects (Table 1), which omit site surveying and lack consistent specification or evaluation of the critical factors for conducting optimal surveys of building stocks. Most previous studies combine data capture methods although the focus is on architectural heritage. Therefore, as expected and as described in the previous section, the case study capturing and processing times are shorter than the times required for heritage buildings, which are often evaluated depending on the point cloud accuracy [29, 77]. The case study timeframes are appropriate for existing non-heritage buildings in need of rehabilitation that require simpler data capture processes, with tighter frames and less precision, to draw up BIMs [23]. By combining TLS and UAV-based photogrammetry, the first step is optimized by reducing the required time and preparation on site. The data processing step introduces new, own-developed algorithms to achieve automatization, great precision, and fast results. Mesh optimization improves the quality of the final results by introducing algorithms that were recently developed by the authors' research group. Table 3 summarizes the pre-processing of the three technologies involved in the survey of the building. The application of the new approach for surveying the exterior of the building entails the use of three data-capturing technologies: TLS, AFE, and HQPs. Consequently, this first application enables a comparison of the results of these data-capturing technologies and their combination. Specifically, seven scenarios were considered: TLS, HQPs, AFE, HQPs + AFE, TLS + HQPs, TLS + AFE, and TLS + HQPs + AFE. The selected parameters to compare the results are number of generated points; capturing time, in minutes; data-processing time, also in minutes; coverage or density; effectiveness or accuracy; and efficiency. The coverage considers the number of points in relation to the total surface area of the building under study. The effectiveness or accuracy of the data captured is the percentage of the number of generated points that are within 1 cm compared to the TLS model. This indicator is related to the effectiveness (understood as the lack of errors) of each of the technologies used for data capture. Finally, the efficiency of each data capture technology employed is the number of points captured per unit of time. Table 4 presents the comparison carried out in this research paper, which shows the advantages and disadvantages of each combination. Appendix A presents Figure A1, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technologies** & **Pre-Processing** & **Alignment** & **Meshing** & **Coloring** & **Overall** \\ \hline  & & **H:M:S** & **H:M:S** & **H:M:S** & **H:M:S** \\ \hline \multirow{3}{*}{AFE} & New algorithm & & & & \\  & (Section S2.2) & 00:09:09 & 00:53:31 & 00:17:04 & 01:45:16 \\ \multirow{2}{*}{HQP} & Yes-Adobe & & & & \\  & Lightroom & 00:11:56 & 01:12:47 & 00:31:42 & 02:44:59 \\ \multirow{2}{*}{TLS} & Yes-Faro Scene & 00:00:00 & 00:02:13 & 00:00:11 & 00:02:31 \\ \hline \hline \end{tabular} Legend: hours (H), minutes (M), seconds (S).  
\end{table} 
Table 3: Summary of the three technologies involved in the data capture of the building exterior.