The surge in demand for upscale apartments in South Korea in the 2000s necessitates the enhanced quality of apartment complexes. Achieving this improvement involves creating diverse spaces within complexes and categorizing them based on spatial arrangement. However, obtaining actual as-built drawings poses challenges, and manual analysis lacks objectivity. The study utilized map API for data collection and Roboflow API for labeling, employing a YOLOv8n-cls model for categorization. Performance evaluation included accuracy, precision, recall, and F1-score values using a confusion matrix. Eigen-CAM was utilized for an analysis that revealed the specific features influencing predictions. The classification model demonstrated relatively high accuracy. Furthermore, the prediction performance was high for lattice and square apartment complexes but low for distributed apartment complexes. These results indicate that a classification model is insufficient for assessing complex characteristics such as the scattered arrangement of building layouts and outdoor spaces, as seen in distributed apartment complexes. We determined that an in-depth analysis of the architectural plans for distributed apartment complexes is necessary to clearly identify their types, and the types must be categorized into several classes, including the distributed type.

Since the 2000s, the demand for enhancing the quality of life of Korean apartment complexes has led to the development of units with diverse outdoor spaces. Analyzing these complexes requires detailed layout data, which are challenging to obtain from construction drawings. This study addresses this issue using the Roboflow map API to collect data based on apartment complex addresses. The dataset, categorized into seven classes, trained a YOLOv8s-seg segmentation model, which was evaluated by precision, recall, and mAP values per class. Detection performance was generally high, although vehicle roads and welfare facilities posed challenges. Segmenting complexes, analyzing main building layouts, and classifying based on period, household count, and regional shape are potential applications. This study is significant because it secured a dataset of layout drawings through maps, a challenging feat given the difficulty in obtaining actual completion blueprints of apartment complexes. However, discrepancies existed between the mapped layouts and the actual blueprints, which caused certain errors; this represents a limitation of the study. Nevertheless, the apartment complex layout analysis model derived from this study is expected to be useful for various future research projects. We anticipate that further studies will be able to conduct architectural planning research on apartment complexes based on an improved analysis model.

In South Korea, the configuration of land parcels within apartment complexes plays a pivotal role in optimizing land use and facility placement. Given the significant impact of land shape on architectural and urban planning outcomes, its analysis is essential. However, studies on land shape have been limited due to the lack of definitive survey criteria. To address these challenges, this study utilized a map application programming interface (API) to gather raw data on apartment complex layouts in South Korea and processed these images using a Python-based image library. An initial analysis involved categorizing the data through K-means clustering. Each cluster’s average image was classified into four distinct groups for comparison with the existing literature. Shape indices were employed to analyze land configurations and assess consistency across classes. These classes were annotated on a parcel level using the Roboflow API, and YOLOv8s-cls was developed to classify the parcels effectively. The evaluation of this model involved calculating accuracy, precision, recall, and F1-score from a confusion matrix. The results show a strong correlation between the identified and established classes, with the YOLO model achieving an accuracy of 86% and demonstrating robust prediction capabilities across classes. This confirms the effective typification of land shapes in the studied apartment complexes. This study introduces a methodology for analyzing parcel shapes through machine learning and deep learning. It asserts that this approach transcends the confines of South Korean apartment complexes, extending its applicability to architectural and urban design planning on a global scale. Analyzing land shapes earmarked for construction enables the formulation of diverse design strategies for building placement and external space arrangement. This highlights the potential for innovative design approaches in architectural and urban planning worldwide.