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.

Automatic Extraction of Indoor Spatial Information from Floor Plan Image: A Patch-Based Deep Learning Methodology Application on Large-Scale Complex Buildings

“…The demand for indoor spatial information is increasing, and automatic floor plan analysis is gaining more attention as an affordable means of acquiring indoor spatial information [2][3][4]. In this context, this study presents a patch-based deep learning network and a framework for reconstructing indoor space for more complex and large-scale buildings as compared to previous studies.…”

“…To meet the high demand for vectorized indoor spatial information, studies on automatic floor plan analysis (i.e., automatically extracting indoor spatial information from floor plan images) have been recently proposed. Floor plans are a good source of indoor spatial information because they are easy to acquire and the automatic techniques based on floor plans are relatively affordable compared to other methods such as light detection and ranging (LiDAR) or manual digitalization [1][2][3]. In fact, a recent study by Kim [3] demonstrated that automatic floor plan analysis technology is more effective in terms of substitutability, completeness, supply, and demand than manual digitalization.…”

“…Furthermore, there is a great demand for digitalized indoor information to obtain digital twins; however, there is a considerable portion of missing digitalized blueprints (e.g., CAD, BIM) in old buildings. Therefore, automatic floorplan analysis is attracting increased attention [3].…”

“…Most existing studies on automatic floor plan analysis have focused on a simple format, which is appropriate for deep learning [4]. The floor plans mostly used in previous studies are CVC (Computer Vision Center), Rakuten, and Rent 3D, whose floor plans are in a simplified format favorable for deep learning [3,4]. Some studies have investigated complicated floor plans, containing diverse fuzzy architectural drawings using EAIS (Electronic Architectural Information System) floor plans [2,4]; however, existing studies have mainly been conducted on house-scale buildings.…”

Emergency Floor Plan Digitization Using Machine Learning

“…The extraction of architectural features has been extensively researched [9][10][11][12]. Hence, for training a model detecting walls and other architectural structures in 2D plans, largescale open-source data sets are already available [13]. In contrast, the detection of emergency symbols on plans supported by object detection has not yet been investigated, and no open-source data set with emergency floor plans is available.…”