An Analysis of South Korean Apartment Complex Types by Period Using Deep Learning

Abstract

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.

Keywords

• deep learning
• YOLOv8
• image classification
• apartment complex