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

Summary

This protocol describes a meta-ensemble deep learning workflow for automated analysis of residential floor plan images. The procedure includes dataset preprocessing, architectural attribute prediction using multiple convolutional neural networks, stacking-based ensemble learning, rule-based residential classification, and explainability analysis using SHapley Additive exPlanations to support residential layout assessment.

Abstract

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.

Introduction

The rapid development of smart city technologies and smart living environments has created a growing demand for automated and data-driven solutions in residential design. In particular, the increasing aging population and the demand for healthcare-oriented housing have heightened the importance of designing residential spaces that are both functional and tailored to the needs of specific user groups^1,2. Conventional architectural floor plan analysis methods are often manual, time-consuming, and dependent on expert interpretation; therefore, they are inefficient and difficult to scale. This has created a strong....

Protocol

This study used a publicly available dataset from the Kaggle platform (Floor Plan Images and Their Details, available at: https://www.kaggle.com/datasets/adilmohammed/floor-plan-images-and-their-details; accessed April 16, 2026). It did not involve human participants, animals, or personally identifiable data; therefore, ethical approval and informed consent were not required.

This protocol presents an AI-driven framework for automated analysis of residential floor plan images, with a focus on spatially aware and healthcare-oriented design. The complete experimental workflow is presented in Figure 1. The protoco....

Results

A set of experiments was performed to evaluate the proposed framework using four DL models, MobileNetV2, InceptionV3, ResNet101, and VGG16, together with the proposed CARE-MIRV-Net meta-ensemble model.

Results of Dataset Acquisition and Preprocessing

The preprocessing phase was designed to prepare the raw floor plan images and related metadata for training and evaluation using DL models. The dataset was obtained from the Kaggle repository and contai.......

Discussion

This study presents CARE-MIRV-Net, a stacking-based meta-ensemble DL framework for the automated analysis of residential floor plan images. The framework integrates four complementary CNN architectures—MobileNetV2, InceptionV3, ResNet101, and VGG16—to predict key architectural attributes, including square footage, number of bedrooms, bathrooms, and garages. These predictions are subsequently combined through a meta-learner and used within a rule-based decision layer to categorize residential layouts into elde.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CARE-MIRV-Net implementation package	Supplementary File 1	Version 1.0	N/A
Figure and table regeneration scripts	Supplementary File 1	Version 1.0	N/A
Floor Plan Images and Their Details Dataset	Kaggle	Public Dataset	N/A
GPU	NVIDIA Corporation	Tesla T4 × 2	N/A
Intel Xeon Processor	Intel Corporation	2.20 GHz	N/A
Keras	Keras Team	Version 3.13.2	N/A
Matplotlib	Matplotlib Development Team	Version 3.9	RRID:SCR_008624
NumPy	NumPy Developers	Version 2.4.6	RRID:SCR_008633
Operating System	Linux	Ubuntu 22.04 LTS	N/A
Pandas	Pandas Development Team	Version 2.3.3	RRID:SCR_018214
Python	Python Software Foundation	Version 3.12.12	RRID:SCR_008394
Scikit-learn	Scikit-learn Developers	Version 1.6.1	RRID:SCR_002577
SHAP	SHAP Development Team	Version 0.51.0	N/A
System Memory (RAM)	Cloud Computing Environment	31 GB	N/A
TensorFlow	Google	Version 2.19.0	RRID:SCR_016345
Training environment configuration file	Supplementary File 1	Version 1.0	N/A