# Massive Data Capture Approach for Modeling Existing Building Stocks
## 5 Discussion
which compares images of the aforementioned seven scenarios regarding their effectiveness, efficiency, and coverage. Former studies have performed similar analyses of indicators such as accuracy, density, and time, as well as overlapping percentages and different flight patterns. However, these are limited to two technologies (TLS and UAV-Base) [31]. The novel approach presented in this research paper differs from these former studies [31] because it reaches a broad overlap of the studied surfaces by taking video images and because it relates drone speed with photograms per second. Consequently, this video technology enables flight path constraints to be discarded. Furthermore, the relative angle between the surface to be scanned and the sensor does not need to be considered. As shown in Table 4, considering similar capture times, data capture using TLS (see Figure 4) is the most effective in terms of precision, as expected from the results of former experiences [78; 79], though it offers the lowest coverage and ranks last in efficiency as a data capture technology. When combined with other techniques (HQPs or AFE), its contribution in terms of coverage and precision does not offer significant improvements and penalizes the efficiency achieved in data capture through the other two technologies. The HQPs shown in Figure 5 provide the highest coverage (density), in line with the previous related literature [80], and greater efficiency. It ranks second in terms of the point accuracy that is obtained. Its combination with AFE seems to be the best option in terms of coverage, and its combination with TLS is the second-best option in terms of efficiency. Finally, automated frame extraction via video (AFE) significantly surpasses TLS in terms of coverage and efficiency but is the least favorable option in terms of precision, as expected from the related literature [81]. Figure 6 presents the resulting model obtained following this technology. Figure 7 presents three 3D views from the same BIM file of the resulting final model, which has a high level of precision. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **TLS** & **HQPs** & **AFE** & **HQPs + AFE** & **TLS + HQPs** & **TLS + AFE** & **TLS + HQPs + AFE** \\ \hline Number of & 5,954,000 & 109,016,806 & 75,777,272 & 157,177,363 & 110,961,813 & 74,058,985 & 155,321,215 \\ Capture time (min) & 50 & 60 & 60 & 120 & 110 & 110 & 170 \\ Data processing & 0 & 40 & 150 & 200 & 60 & 180 & 220 \\ time (min) & 50 & 100 & 210 & 320 & 170 & 290 & 390 \\ \hline Coverage & & & & & & & \\ (no. points/total & 1461,20 & 26,754.26 & 18,596.81 & 38,373.54 & 27,231.59 & 18,175.12 & 38,118.02 \\ surface (m\({}^{2}\))) & & & & & & & \\ Effectiveness & & & & & & & \\ (accuracy) (\% of & 100,000) & 21.17\% & 5.34\% & 19.56\% & 18.83\% & 18.27\% & 20.94\% \\ points 1 cm closer & & & & & & & \\ Efficiency & & & & & & & \\ (No. points/min) & & & & & & & \\ \end{tabular} Legend: green are positive features being the darkest the most positive; red are negative features being the darkest the most negative.  
\end{table} 
Table 4: Comparison of the data-capturing technologies and their combination. Consequently, it could be considered in further applications beyond this research project, such as modeling a digital twin (DT) [82] of the original building. Figure 8 presents a general view from overlapping the BIM and the point cloud from TLS. Figure 9 compares the final model to a manually modeled BIM. The figure shows in detail the misalignment and unevenness of the structure in both models. Figure 4: Model resulting from the scanner laser (TLS). Figure 5: Model resulting from UAV and photography images (HQPs). Figure 6: Model resulting from UAV and video images (AFE). Figure 7: Final model. Left view presents the level of detail of the building. Top right shows the result from TIS overlapped with the geometric information. Bottom right depicts a whole view of the final BIM. The BIM elaborated in the project was only intended to assert geometric information.