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.
Highlights
Digitalized plans from light detection and ranging (LiDAR), building information modeling (BIM), or computer aided design (CAD) are certainly useful sources to recreate indoor spaces; there is an unignorable proportion of missing digital blueprints of old buildings, which results in considerable effort to draw the blueprint image into digital plans
The main contribution of this study is to evaluate the list of criteria and factors for assessing the value of the floor plan analysis technology
This study developed a technology hierarchy assessment model and identified important criteria and their weights to evaluate the relative importance of the factors
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. It is possible to recreate indoor information through LiDAR systems It needs to be executed at the site, owing to the high cost and time required; only a few buildings can be modeled using this method [3]. Floor plan images generally have considerable noise, auxiliary lines, and symbols In these circumstances, Kim 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). Before the development of this technology, extracting indoor spatial information from floor plan images could be achieved through manual digitization. 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. Using the relative importance of each criterion and factors of technology value derived from the AHP analysis and the scoring of each element, the distinction between automatic and manual extraction is investigated
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