# Developing a Robust Training Dataset for AI-Driven Architectural Spatial Layout Generation
## 2 Related Research
### Al-Based Spatial Layout Generation Research  
[PERSON] [15] defines AI as the intelligence of a machine or computer that enables it to imitate or mimic human capabilities. Regarding this perspective, AI-based tools are divided into \"intelligent systems\" or \"knowledge-based systems\" and \"computational intelligence and hybrid systems\". Intelligent systems are highly relevant in design automation [16].  
This study delves into recent deep-learning spatial layout generation studies in the field of computer vision. [PERSON] [2] trained a model to generate a footprint, proceeded with a generative adversarial network (GAN)-generated master plan for program repartitioning, and proposed a furniture layout. After the floor plans, doors, windows, and vertical circulations were placed, a layout was created using GAN learning. [PERSON] [3] studied a house GAN layout that used an architectural constraint as a graph (i.e., the number and types of rooms with their spatial adjacency) and produced a set of axis-aligned bounding boxes of rooms. [PERSON] [4] determined the location of a room as a point given a boundary and predicted the location of a wall using an encoder-decoder model. By repeatedly learning to create different rooms, this study constructed a vector dataset for more than 80,000 residential floor plans. [PERSON] [17] utilized the CubiGraph5k [11] dataset. Utilizing machine-parsing structured floor plan data and Graph Convolutional Networks (GCN), an approximate spatial layout is generated when a user draws rooms (kitchen, living room, etc.) as nodes like a graph. [PERSON] [18] learned drawing elements and spatial usage using the DeeplabV3+ model and then automatically generated spatial zoning based on AttnGAN. [PERSON] [5] learned to generate floor plans from layout graphs using the RPLAN's dataset [4]. Given an input building boundary, a floor plan associated with the layout graphs is generated from a database.  
A unifying aspect of prior studies is a rule-based randomly generated floor plan. However, these studies did not consider design theory or knowledge. Although AI-based intelligent systems boast rapid result generation, their results often lack precision and are unreliable. For better quality and more reliable plans, it should be developed as an AI-based knowledge system with high reliability. This necessitates the construction of training datasets consistently rooted in vector data.  
The training datasets should include complex vector data, such as type, size, scale, location, and adjacency of space, rather than simply drawing elements, such as walls, doors, windows, and furniture. This is because the more detailed the vector data about space that are constructed, the better the AI can learn these spatial relationships through neural networks. Moreover, the current study differentiates itself by introducing a novel algorithm designed to convert raster-based floor plan images to vector-based data using deep learning, providing a strategy for formulating expansive training datasets tailored for high-precision AI applications in architectural design. Such insights will be instrumental in generating or recommending spaces in the future.