# Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation
## 1 Introduction  
The use arrangement of a building space is created based on the expertise and experience of a design expert, and is a complex result of a wide variety of factors related to the use, scale, environment, and regulations of the building itself [1]. Therefore, automating architectural design has a lot of difficulties because it has a deep relationship with humans own creative domain. This is because it is difficult to describe and express architectural drawings with a few simple rules because very complex knowledge and experience must be combined. As a modern machine learning method, deep learning is increasingly used to achieve the state-of-the-art performance in many fields including computer vision, natural language processing and bioinformatics [2]. Recently, with the rapid development of artificial intelligence technology, there are increasing attempts to apply artificial intelligence technology to urban and architectural design [3]. However, in the field of architecture, research such as design automation has been continuing for a long time, but it is difficult to find remarkable growth or development announcements [4]. Artificial intelligence presents an optimized alternative by performing problem-solving knowledge and problem-solving processes under specific conditions. This makes it possible to creatively examine various design alternatives under conditions that satisfy the functional requirements of the building [5]. In this study, in order to develop an architectural design automation technology using artificial intelligence, the characteristics of architectural drawings, that is, the architectural elements and thecomposition of spaces expressed in the drawings, were learned, recognized, and inferred through deep learning. Classification, localization/detection, and segmentation are image-related techniques most widely used in computer vision. Classification is a task of predicting a label for an input, Localization/detection provides information by drawing a bounding box at the spot where the object is located predicting the label for an object. Semantic segmentation supports many computer vision applications across multiple domains, as the identification of the region of interest increases the accuracy of the final result. Semantic segmentation has been one of the leading research interests in computer vision recently [6]. Semantic segmentation does not represent an object in a static range such as a bounding box but predicts the object in pixels and separates the target pixel area into meaningful units when detecting an object in an image. It not only checks which object exists in the image in a semantic unit, but also identifies the spatial feature of the object and reflects it in the segmentation result [7]. Therefore, in the case of drawings representing architectural spaces with many symbolic expressions, semantic segmentation is considered to be a suitable technique for grasping the relationship between space and space or between drawing elements. Many semantic segmentation approaches are based on CNNs (convolutional neural network) and FCN (fully convolution network). CNNs are at the core of an FCN, which employs forward skip connections from the downsampling layers to the upsampling layers to offset the downsampling effect and thus avoid feature deterioration and enhance the inference process. But, FCN shows limitations with time, such as mismatched relationships, and unclear classes. To avoid the limitations of FCN resolution, Deeplab consider two types of neural networks that use spatial pyramid pooling module or encoder-decoder structure for semantic segmentation, where the former one captures rich contextual information by pooling features at different resolution while the latter one is able to obtain sharp object boundaries [8; 9]. The biggest problem in applying deep learning in the field of architectural design is that the amount of publicly disclosed data is absolutely insufficient and that the publicly disclosed data also has a wide variety of forms [10].