Deep-Learning-Based Prediction of Residential Floor Plan Attributes for Elderly-Oriented Housing Assessment

The growing demand for smart housing solutions for aging populations has increased the need for automated methods for residential floor plan analysis. Conventional architectural evaluation approaches are often manual, time-consuming, and difficult to scale, highlighting the need for artificial intelligence-based techniques that can interpret spatial layouts and support residential design assessment. This protocol describes a meta-ensemble deep learning framework for automated analysis of residential floor plan images. The proposed CARE-MIRV-Net framework integrates four complementary convolutional neural networks-MobileNetV2, InceptionV3, ResNet101, and VGG16-to predict architectural attributes, including square footage, number of bedrooms, bathrooms, and garages. Predictions from the base models are combined through a stacking-based meta-ensemble model and subsequently used within a rule-based decision layer to categorize residential layouts as elderly care, medical care, or general residential. Experimental evaluation demonstrated robust predictive performance across multiple target variables. The proposed model achieved a mean absolute error (MAE) of 432.48 and a coefficient of determination (R²) of 0.7053 for square footage prediction. For bathroom prediction, the framework achieved R² of 0.7605, while garage prediction yielded the lowest MAE of 0.1697 and the highest R² of 0.6958. Qualitative analyses further demonstrated the ability of the framework to generate architectural attribute predictions and support application-oriented residential layout assessment. To enhance transparency and interpretability, SHapley Additive exPlanations were incorporated to quantify the contribution of each base model to the final predictions. The proposed framework provides an interpretable and scalable approach for residential floor plan analysis and decision support.