# Method for Constructing a Facade Dataset through Deep Learning-Based Automatic Image Labeling
## 1 Introduction  
The current construction industry has made great progress through the accumulation of various computer programs and construction data, including computer aided design (CAD) [1]. However, compared with the manufacturing sector, labor productivity is low, except in the construction industry [1, 2]. Artificial intelligence (AI) must be used to improve areas of architecture that are considered human aspects by intelligent computer systems to increase productivity. However, the level of AI research and technology development in the field of architectural design is only at the level of research for optimizing and streamlining tasks in the design-construction phase or automation of some simple architectural designs. The development of artificial related technologies based on design uniqueness is insufficient [3].  
In this study, we determined that large scale facade datasets can be constructed with considerably less effort than any other methods by applying AI. One of the most important causes of the low level of research AI in the field of architecture is the lack ofquantified constructed datasets, which are essential components of artificial neural network learning [4]. Currently, the data in the field of architectural design are mixed in various data formats such as drawings, images, and texts. Most of the available design information accumulated in the existing field of architectural planning is qualitative and depends on the expert knowledge of construction workers [5]. Therefore, in order to use construction datasets for AI research, it is essential to quantify the design knowledge and express it in a form that can be recognized by a computer. Advanced research on AI-based construction technology requires the quantitative construction of architectural construction datasets as the initial step.  
Building information modeling (BIM) technology, which is an architectural information management platform, is an important vehicle for connecting distributed construction data to digital models. BIM building information can be managed in a form that can be recognized by a computer. However, BIM models are limited in their utilization to the BIM process [6]. However, unlike special processes such as BIM, there is a way to build and utilize large amounts of data without investing a lot of time and capital through machine learning and by using public data released by governments in various countries [7]. An example of a machine-learning-based technology is Urbanbase, which automatically converts 2D drawings to 3D models [8]. In this study, we determined that large scale facade datasets can be constructed with considerably less effort than any other methods by applying deep learning technology and utilizing non-quantified street facade images existing in road-view images generated from web portals such as Google Street View [9] and Naver Street View [10].  
By utilizing the constructed datasets, a facade database can be constructed to identify the characteristics of the facade designs of buildings on a selected street, and the facade design information can be extracted directly from this database. Additionally, the constructed database can be used in various related fields. The facade data were directly collected through road-view images and automatically labeled using deep learning. Through this process, a method for efficiently constructing facade datasets was proposed. Simultaneously, we examined the applicability of the facade datasets in calculating the amount of data required by the facade database to determine the design characteristics of the streets in Dongseong-ro, Daegu Metropolitan City.