# Massive Data Capture Approach for Modeling Existing Building Stocks
## 1 Introduction  
The rehabilitation of buildings is a current global need [1, 2, 3] that could contribute to mitigating the negative economic, environmental, and social impacts of the building sector [4]. For instance, the energy consumption of buildings is continuously increasing. Buildings were responsible for 34 percent of global demand and 37 per cent of energy and process-related CO2 emissions in 2022 [5]. In recent years, one of the main strategies for established cities to achieve considerable greenhouse gas emissions savings is to efficiently improve, reuse, or modernize their building stock, of which 20 percent needs to be renovated to become zero-carbon-ready by 2030 [6]. In consequence, there are multiple initiatives in motion to incentivize the refurbishment of buildings, such as international agreements from conventions like COP27 [7]; specific regions' targets, regulations, and directives like those of the European Union [8, 9]; and public policies [10]. There are alsonumerous completed and ongoing refurbishment projects that focus on increasing the sustainability of architecture [11]. However, many cases address buildings as isolated entities with no connection to their urban settings. Initiatives such as the New European Bauhaus [12] recognize that innovative solutions for the building sector should be supported by a deep understanding of the existing building stock's performance. The rehabilitation of this stock can focus on one or several multiple issues such as the building's energy performance [13], its structural behavior [14], fire safety, life cycle analysis [15], and lighting. Most rehabilitation processes rely on digital simulations of the buildings regarding the aforementioned multiple issues. Therefore, digital models of existing buildings are required, such as building information model (BIM) files [16]. The use of BIMs could represent an opportunity to improve the financial and cost performance [17], and environmental impacts in all steps of a building's life [18]. Given the recent rise in publications and citations, there is increasing interest in these fields among the scientific community [19]. Current documentation on existing building stocks that require rehabilitation is in printed format. Therefore, digital models need to be produced. One option is to create BIMs from scratch. However, this has limitations; for example, the resulting model is an abstract theoretical version that cannot reproduce the geometric singularities of the real building. This has implications for structural performance; see, for instance, [20]. Nevertheless, the automation of this digitalization is an interesting solution that has been considered for decades [21]. Computer vision technology and three-dimensional (3D) data capture have been widely utilized for construction applications, as they can capture geometric, scale, and shape information [22]. However, there is still a lack of interoperability between BIMs and these optical data capture technologies, including photogrammetry, terrestrial laser scanning (TLS) [23], or ground penetrating radar (GPR), which could help to optimize the survey processes of existing building stocks. Table 1 includes recent related research projects [24] at the building scale, excluding those focused exclusively on building parts such as facades [25,26], roofs [27], or the structure [28]. The use of TLS is highly precise but time-consuming and not applicable when speed is required. Moreover, in line with Table 1, previous reviews on TLS [39,40] indicate that most research projects on the obtention of BIMs focus on heritage buildings and complexes. While Scan-to-BIM, which consists of digitally capturing a building by means of a 3D laser