# Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation
## 6 Conclusions  
This study was intended to obtain large quantities of datasets by using deep learning for the automatic training with architectural drawings and enabling the automatic recognition, inference and labeling of the drawing elements and space usage represented on architectural drawings. To that end, GOOGLE DeepLabV3+ was used with the floor plans of apartment houses in the experiment. As a result, the accuracy of the inference of such elements as walls, doors, windows and sliding doors from the floor plan images reached 87.83%, while the accuracy of the inference of such space as living rooms, rooms, bathrooms, balconies, and entrances was 92.09%. The low accuracy found in this study was analyzed in terms of three aspects. First, the data were insufficient. Excluding government-owned corporations such as Korea Land & Housing Corporation, private companies as well as their design sub-contractors hardly publish their architectural drawings and relevant data, which is adverse to the collection of floor plan data. Therefore, in this study, the pixel shifting was conducted for data augmentation as well as other applicable techniques such as flipping or rotation. Second, as for the size of the training data, larger images led to low accuracies. Subsequently, the re-training with the re-sized smaller images proved conducive to higher accuracies. This finding warrants the need for the additional pre-processing of limiting the sizes of images. Yet, when not only the geometry, but also the dimensionality on architectural drawings is to be used, the scales need be kept intact. Any random adjustment of the sizes of images makes it difficult to recognize the scales, which should be taken into account. Third, the accuracy was lower on the drawings where each space was unlabeled than those where the name of each space was present. This result seems attributable to the fact that the difference between the rooms and living rooms on the drawings is vague to the naked eye. To address the challenge, as aforementioned, large amounts of data with names unspecified may be used for training. Alternatively, the hybrid approach of incorporating the deep learning-based image segmentation and the rule-based coding is applicable as attempted in this study. The hybrid approach has already been documented and proved more accurate than the approach of using the deep learning only. In the case of rooms and living rooms that are not easy to distinguish based on their geometric forms, rules such as the number or geometry of openings and the correlation between rooms may be added to increase the accuracy. Each of the two experimental categories proposed in this study is applicable in practice. First, the recognition of the drawing elements is applicable to the automatic generation of a 3D model for each object, combination of the elements, and future automation of 3D modeling. Also, the recognition of walls and openings is applicable to generating the evacuation paths and calculating the evacuation distances. Moreover, it may be applicable to the automatic calculation of the window and wall areas and the analysis of building energy ratings involving window area ratios. Second, the recognition of space enables the training with large quantities of architectural design plans and allows the generation of virtual drawing images using network models including GAN. If the rules necessary for constructing the architectural space are added to the process, some construction plans fit for specific situations or requirements can be proposed, which will lay the foundation for future AI-based architectural design techniques. This study is a study to construct a learning data set essential for deep learning implementation as an early stage for developing artificial intelligence architectural design technology. Using the method proposed in this study, it is possible to generate a large amount of labeled data, and it can be used to implement artificial intelligence-based architectural design.