Abstract
Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded.
Highlights
With the recent developments in technology, including Internet of Things, location tracking and location-based services such as networks and navigation are expanding indoors
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]
Kim [3] demonstrated that automatic floor plan analysis technology is more effective in terms of substitutability, completeness, supply, and demand than manual digitalization
Summary
With the recent developments in technology, including Internet of Things, location tracking and location-based services such as networks and navigation are expanding indoors. 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]. Kim [3] demonstrated that automatic floor plan analysis technology is more effective in terms of substitutability, completeness, supply, and demand than manual digitalization. Automatic floorplan analysis is attracting increased attention [3]
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