# Massive Data Capture Approach for Modeling Existing Building Stocks
## 2 Methodology
### S1--Data Capture  
The different data capture technologies for the studied building and the two data capture sources for the site are depicted in Figure 1. To capture building data, the main novelty of the approach in this step, S1, is that it combines for the first time, to the authors' best knowledge, data capture from all the following technologies: (a) infrared scanning, which is also known as thermal infrared sensing (TIS) [45], or laser scanning, known as terrestrial laser scanning (TLS) [30]; (b) a topo survey, which includes surveying applications such as total station, real-time kinematic (RTK) positioning [46], and a global navigation satellite system (GNSS) [47]; and (c) image capture for photogrammetry using high-quality pictures (HQPs) and automatic frame extraction (AFE) from video, both using UAVs [48].  
Some previous projects have combined TLS or TIS with photogrammetry, using photographs rather than videos [26; 49]. These approaches allow for the combination of captured data with topographic surveys after the reconstruction process, if needed [50]. However, they require considerable field planning and separate processing of the measurements obtained using different surveying applications, such as the aforementioned total station, RTK and GNSS, to validate the accuracy of the data.  
Compared to the former methods of conventional photogrammetry [43], this new approach does not need the planning of topographical control points because the data obtained from the TLS are joined to the georeferenced data from LiDAR in Step 2, as explained in the next section. In contrast, existing approaches capture data from manual  
Figure 1: Workflow of the new approach presented in this research project.  
positioning [51], unmanned aerial vehicles (UAVs) [32], and unmanned aircraft systems (UASs) [52], among others. To the authors' best knowledge, to date, very few approaches have allowed for the vertical automated data capture required for facades [29] and none can control the occlusion of elements with their automated approaches. This approach can control occlusions and the full coverage of the building during flight.  
In the case of AFE from video, the primary innovation introduced by this methodology involves the automated acquisition of a larger volume of data through a self-programmed algorithm that establishes a correlation between the video frame number and its corresponding position. Other significant advantages of using a UAV are (1) faster and more agile data capture; (2) no need to manually position the drone to ensure correct overlap between photos; and (3) a less invasive method than manually capturing photos to achieve the same coverage. The procedure for acquiring video frames is based on the automatic filtering, selection, and optical correction of frames obtained from videos captured with a drone, which is part of the next step, as described in the following section. The drone's flight speed is constant, with frequent video recording of captured frames per second and high resolution. This implies a specific variation in the sensor's position from one frame to the next, and this acquisition procedure relates the frame numbers to the drone's position in space to control the desired overlap between photos and coverage of the survey. The resulting overlap following this procedure is much greater than that obtained in photogrammetry with manual positioning or existing automatic procedures.  
During site data capture, cartography [53] and/or satellite light detection and ranging (LiDAR) [54] are used. Most projects use these sources because they are generally available and provide precise, georeferenced data for the site [55].  
Once the data capture is completed, this first step can result in three types of information: (i) point clouds, for example in.PTX format, from the TLS and TIS; (ii) control point coordinates from the data survey; and (iii) video and pictures files, for example in.MOV or JPG format obtained from the drone.