# Evaluation of Deep Learning-Based Automatic Floor Plan Analysis Technology: An AHP-Based Assessment
## 3 Research Framework
### Hierarchy Structuring  
The proposed model for the evaluation of floor plan analysis utilizes a hierarchy of evaluation elements, as shown in Figure 4. The hierarchy reflects the objective concerns in the decision-making process in developing a set of criteria to evaluate the technology value. The criteria and factors are derived mainly based on the Technology Valuation Program for the R&D Result Diffusion report by the Korea Institute of Science and Technology Evaluation and Planning (KISTEP) [28] and they are consistent with the characteristics of the present technology. In addition, an FGI was conducted among spatial information experts, including automatic floor plan engineers and manual editors to: (1) identify the distinctive differences between and the strengths and weaknesses of each technology and (2) determine the criteria and factors for floor plan analysis technology evaluation. Table 1 presents the strengths and weaknesses of the manual and automatic approaches that were concluded from the FGIs. In manual editing, a drawing editor engineer manually creates and edits based on a floor plan image to generate indoor spatial information in vector format. In automatic editing, floor plan images are trained using a deep learning algorithm, and the learned segmentation results are vectorized to generate indoor spatial information. The main advantage of the automatic approach is that once it is trained, it enables the processing of a vast number of drawing images with little marginal cost. However, only the major building elements like room structures, junctions, walls, and openings based on the information provided in the floor plan image can be extracted automatically. The manual edit can generate additional information that is not stated in the floor plan image, if necessary. According to [PERSON] [29], corporate resources must have the following four requirements: (1) value, (2) ratio, (3) non-importability, and (4) non-substitution to gain continuous competitive advantage and greater benefits over competitors. [PERSON] [28] suggested an aspect of technology evaluation from [PERSON]'s by combining value and rareness under technology superiority, non-imitability and non-substitution under technology exclusivity, and adding technology constraints. Table 2 presents the aspects of technology evaluation and the criteria and factors suggested in [30]. Figure 4: Hierarchy of floor plan analysis technology value. Technology value assessment is primarily evaluated in three categories: technology superiority, technology exclusivity, and technology constraints [30]. Among them, the technology constraints assess the competitive and socioeconomic constraints that may arise during commercialization and utilization of the developed technology. This technology is developed on an open-source basis for public purposes rather than commercialization purposes; therefore, a suitable hierarchy of technology value assessment is constructed by selecting the appropriate detailed factors from technology superiority and technology exclusivity criteria. Technology superiority assesses the superiority of the technology, to judge how unique it is compared with existing technologies. Factors that determine the superiority of this technology include technology completeness, differentiation, applicability, and transferability. Through FGI, experts stated that it would be better to judge the superiority of floor plan analysis technology in terms of completeness and differentiation rather than industry expansion, facility investment size, and technology transfer because the target technology is far from being developed for commercialization investment or technology transfer purposes. Therefore, this is reflected in the hierarchy structuring by considering the level of technology development, completeness of technology, novelty, originality, and efficiency.