In this study, technology that constructs vectorized indoor spatial information from raster floor plan images is defined as floor plan analysis technology. This technology can be divided into two approaches: manual and automatic. The process of the study goes through three stages: (1) formulating a hierarchical model containing assessment criteria and factors; (2) refining the measures of criteria and factors and identifying the weights of criteria and factors through AHP; and (3) scoring both methods (i.e., manual and automatic).
Figure 2 shows the manual and automatic approaches in terms of extracting indoor spatial information from floor plan images. In this study, EAIS data (a complicated floor plan with emphasis on practical value) were employed as the target floor plan image.
In phase 1, to construct the hierarchy of technology value, both literature reviews and focus group interviews (FGIs) were conducted, considering the characteristics of the technology for both methods. In phase 2, after deriving key factors and criteria and their hierarchy for technology evaluation, a survey on AHP was conducted with experts in the field of spatial information. With the relative weights derived from AHP, assessment of each method and comparison of their characteristics was conducted in phase 3. To score both methods (i.e., automatic and manual), an additional survey was conducted. The overall process is illustrated in
Figure 3.
3.1. 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 Barney [
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. KISTEP [
28] suggested an aspect of technology evaluation from Barney’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].
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.
Technology exclusivity assesses whether there is any difficulty in exercising exclusive ownership and using the technology, along with legal rights. As this technology was developed on an open-source basis for public purposes through national R&D projects, FGI concluded that it is better to examine it in terms of alternative possibilities and supply and demand aspects rather than considering legal rights and technology protection.
The classification, detail, and hierarchy of technology valuation are as follows. There are four types of criteria: completeness, distinctiveness, substitutivity, and supply and demand. The factors consist of a total of 10 categories: technical development phase, completeness of technology, commercialization potential under the completeness criteria, novelty, originality, efficiency under the distinctiveness criteria, possibility of existence of similar technologies, possibility of new technologies to emerge under the substitutivity criteria, and finally demand from customers, and supply from providers under the supply and demand criteria. The proposed model for the evaluation of floor plan analysis technology with a hierarchy of evaluation elements is shown in
Figure 4, and the individual factors are defined in
Table 3.
3.2. Relative Weight Calculation
When all the elements (criteria and factors) referring to the same hierarchical level are compared in pairs, relative weights are assigned through the construction of the matrices of the comparison pairs. A pairwise comparison is used because the psychologist claimed that only two alternatives are easier and more accurate in expressing public opinion than simultaneously employing all alternatives [
31]. For comparing an element in a group on one level of the hierarchy with respect to an element at the next higher level, an n × n matrix is constructed, where n is the number of elements in the group.
The result of the single comparison is an a
ij dominance coefficient, which expresses a measure of the relative importance of element i with respect to element j. A pairwise comparison was performed to determine the weight for prioritizing components, and a nine-point scale was used for relative evaluation (see
Table 4).
The estimation of relative weights is derived by performing
nC
2 times of two-way comparison of n evaluation items at one level of decision makers, which can be used to construct the pairwise comparison matrix A
n×n. The square matrix a
ij is an estimate of the relative weight w
i/w
j of i for element j, and matrix A is a
ij = 1/a
ji, a reciprocal matrix where the elements in the main diagonal are all equal to 1. These square matrices allow for eigenvectors and eigenvalues, which are used as a means of determining priorities and measuring consistency in judgment, respectively. The key to the stratification analysis is the extent of transitive consistency that can be maintained, and this can be verified through the determination of the consistency ratio (CR), which divides the consistency index (CI) of the result using a random index (RI). CI is calculated as (λ
max − n)/(n − 1), where λ
max is the largest eigenvalue of the two-way comparison matrix, and n denotes the number of criteria being compared. Contrastingly, RI is the average value of the CIs of the comparative matrix constructed using random numbers from 1 to 9, which refers to the dimensional average random index of the matrix. In a two-way comparison matrix of stratification analysis, the relationship of λ
max ≥ n is always maintained. For comparison matrices with perfect consistency, λ
max = n and the greater the consistency, the closer the λ
max is to n; it is possible to measure the degree of consistency using equation (2). The smaller the value of CR, the higher its transitive consistency. Saaty judged that people performed a two-way comparison fairly consistently if the CR was within 10% (0.1), and an acceptable level of inconsistency was within 20% (0.2); however, a lack of consistency above 20% (0.2) required re-examination [
33]. In this study, only CR results within 0.1 were utilized.