# Evaluation of Deep Learning-Based Automatic Floor Plan Analysis Technology: An AHP-Based Assessment
###### Abstract  
This study proposes a technology that allows automatic extraction of vectorized indoor spatial information from raster images of floor plans. Automatic reconstruction of indoor spaces from floor plans is based on a deep learning algorithm, which trains on scanned floor plan images and extracts critical indoor elements such as room structures, junctions, walls, and openings. The newly developed technology proposed herein can handle complicated floor plans which could not be automatically extracted by previous studies because of its complexity and difficulty in being trained in deep learning. Such complicated reconstruction solely from a floor plan image can be digitized and vectorized either through manual drawing or with the help of newly developed deep learning-based automatic extraction. This study proposes an evaluation framework for assessing this newly developed technology against manual digitization. Using the analytical hierarchy process, the hierarchical aspects of technology value and their relative importance are systematically quantified. The analysis suggested that the automatic technology using a deep learning algorithm had predominant criteria followed by, substitutability, completeness, and supply and demand. In this study, the technology value of automatic floor plan analysis compared with that of traditional manual edits is compared systemically and assessed qualitatively, which had not been done in existing studies. Consequently, this study determines the effectiveness and usefulness of automatic floor plan analysis as a reasonable technology for acquiring indoor spatial information. a +
Footnote †: journal: Applied Sciences  
arxiv  
Footnote †: journal: Applied Sciences  
can automatically learn patterns from data and offer robustness in performance against noise [3; 5]. However, the floor plans previously targeted in learning-based research fields were limited to simple and unified formats because it is favorable to compose test sets suitable for the training of deep/machine learning [5]. For practical use, floor plan images generally have considerable noise, auxiliary lines, and symbols. In these circumstances, [PERSON] et al. [5] proposed a new technology based on deep learning algorithms to analyze complicated floor plans with extensive and complex indoor structures by employing style transfer and conditional generative adversarial networks (GANs). In this study, critical indoor spatial information such as room structures, junctions, walls, and openings could be extracted automatically and converted into a 3D data model that is compatible with a standard 3D data model, such as IndoorGML (Indoor Geographic Markup Language). Before the development of this technology, extracting indoor spatial information from floor plan images could be achieved through manual digitization. Manual measurements are time- and labor-demanding and therefore are either very expensive or unreliable when applied comprehensively on sites [6]. However, there is hardly any precedent study that comparatively evaluates automatic and manual digitalization of floor plans despite a growing body of literature introduced in the field of floor plan analysis. In fact, technology evaluation of existing studies on floor plan analysis was mainly based on internal comparisons between automatic floor plan analysis only. This study expands the scope of the research of [PERSON] et al. [5] by considering the value of the developed technology compared with traditional manual digitization. Accordingly, an assessment of the indoor information extraction technology is performed considering the technological characteristics and hierarchy of technology value. Considering the key factors for assessing the technology value, a quantitative evaluation method using the analytical hierarchy process (AHP) is presented. Through this study, an evaluation framework for assessing technologies that extract indoor spatial information from floor plan images can be developed, which has not been systematically demonstrated in existing literature.