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 need for intelligent systems capable of automatically analyzing residential layouts and supporting informed decision-making³.

Recent advances in artificial intelligence (AI), particularly in deep learning (DL) and computer vision, have significantly improved the analysis of image-based data. Convolutional neural networks (CNNs) have proven highly effective for spatial feature extraction and the learning of complex visual patterns, making them suitable for floor plan image analysis^4,5,6. These models can learn implicit representations of room structures, layout organization, and spatial relationships that are essential for predicting architectural attributes^7,8. However, reliance on a single DL model may limit generalization because different architectures may focus on distinct spatial features and exhibit biases arising from architectural variability⁹. Despite these advances, existing approaches often lack an integrated framework that combines multiple models, supports multioutput prediction, and provides application-level interpretability. Furthermore, healthcare-oriented residential assessment and explainability have received relatively limited attention within floor plan analysis systems. The absence of such comprehensive frameworks limits practical applicability and interpretability, particularly in domains where transparency and decision support are important^10,11,12.

AI has also contributed substantially to the automated analysis and generation of architectural floor plans. One study investigated the use of machine learning (ML) to transform two-dimensional floor plan images into three-dimensional models¹³. The authors highlighted the limitations of conventional computer-aided design (CAD)-based methods and emphasized the importance of scalable AI-based pipelines for spatial understanding. Their findings demonstrated that DL can effectively extract structural information from floor plans, although challenges related to generalization and data variability remain. Another study proposed a multiagent deep reinforcement learning framework for automated floor plan generation¹⁴. Although the approach offers considerable flexibility and customization for architectural design, it is primarily focused on design generation rather than predictive analysis of existing floor plans.

A comprehensive floor plan retrieval survey examined semantic, spatial, shape-based, and texture-based features for floor plan understanding¹⁵. The survey emphasized the importance of DL models, including CNNs and transformer-based architectures, for learning meaningful representations. However, it also identified challenges related to noise, distortion, and feature discrimination in complex layouts. Similarly, an ML-based approach incorporated topological and visual relationships for floor plan recognition⁸. The study demonstrated that visual accessibility and spatial connectivity are important for understanding architectural layouts, but it did not address multioutput prediction or ensemble-learning strategies. Generative AI has also been explored in architectural applications. A review of generative AI in architecture examined the use of generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models for design automation¹⁶. The authors highlighted the increasing adoption of AI tools in practical architectural workflows. However, the focus of that work was design generation rather than predictive modeling or decision-support applications. Explainable AI has become increasingly important for interpreting DL models. A study proposed a SHapley Additive exPlanations (SHAP)-based framework for interpreting DL models in urban simulation applications¹⁷. The results demonstrated that SHAP can identify influential features and improve interpretability at both the global and local levels. Although the study supports the value of SHAP in spatial applications, it did not investigate its integration with ensemble-learning frameworks. Recent developments in DL architectures have further improved performance in image-based tasks. A study presented a comprehensive review of contemporary DL models including CNN-based and transformer-based architectures¹⁸. The study highlighted the importance of model selection and hybrid architectures for improving generalization performance. However, it did not specifically address floor plan analysis applications. Large-scale datasets have also facilitated advances in architectural analysis. For example, a study introduced a benchmark dataset containing thousands of complex floor plans¹⁹. The study demonstrated that existing models continue to face challenges when analyzing large-scale and multiapartment layouts, highlighting the need for more robust and generalizable approaches.

DL has also been extensively studied in ensemble learning. A recent study on deep ensemble methods indicated that combining multiple models can significantly improve predictive accuracy and robustness by reducing bias and variance²⁰. Despite their effectiveness, these methods have not been widely applied to architectural floor plan analysis, particularly in multioutput regression tasks. Furthermore, recent studies in automated floor plan analysis have highlighted the importance of integrated frameworks that combine prediction, classification, and interpretability^21,22,23. Existing research typically focuses on individual functions, such as feature extraction, layout generation, or classification. Consequently, a clear research gap remains in the development of a unified framework that integrates multimodel learning, residential-layout assessment, and explainable AI. Although considerable advances have been made in DL and architectural analysis, several research gaps remain. Most existing studies focus on floor plan recognition, feature extraction, or layout generation without providing a unified framework that supports both multioutput prediction and application-level decision-making. In addition, many approaches rely on a single DL model, which may limit their ability to capture diverse spatial characteristics and may introduce model-specific biases. Ensemble learning for floor plan analysis remains relatively underexplored, particularly regarding the integration of heterogeneous models to improve robustness. Furthermore, limited attention has been given to residential classification with an emphasis on elderly-oriented housing considerations and interpretability. Since many DL models function as black boxes, understanding the basis of their predictions remains challenging. These limitations highlight the need for a comprehensive framework that integrates multimodel learning, accurate prediction, application-oriented residential classification, and explainable AI.

This study presents an automated framework for the analysis of residential floor plan images, with an emphasis on spatial awareness and residential-layout assessment, to address these challenges. The proposed framework introduces a meta-ensemble DL model, CARE-MIRV-Net, which integrates four complementary CNNs—MobileNetV2, InceptionV3, ResNet101, and VGG16—within a stacking-based architecture^24,25,26,27. Each model independently predicts key architectural attributes, including square footage, number of bedrooms, bathrooms, and garages. These predictions are subsequently combined by a meta-learner to generate refined predictions. In addition, a rule-based decision layer is used to categorize residential layouts as elderly care, medical care, or general residential based on the predicted attributes.

The contributions of this work are as follows. First, a meta-ensemble DL framework is proposed for the multioutput prediction of architectural attributes from residential floor plan images. Second, the CARE-MIRV-Net model integrates four CNN architectures to improve prediction accuracy and generalization. Third, a rule-based decision layer is incorporated to support application-oriented residential layout categorization based on predicted architectural attributes. Fourth, SHAP-based explainable AI is integrated to enhance model transparency and interpretability. Finally, extensive experiments are conducted to evaluate the proposed framework using mean absolute error (MAE) and the coefficient of determination (R²) as performance metrics.

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 protocol is designed to bridge the gap between low-level architectural feature prediction and high-level application-oriented decision-making. It integrates DL, ensemble modeling, and explainable AI to provide an accurate, robust, and interpretable solution for analyzing residential layouts. The framework consists of multiple stages, beginning with data acquisition and preprocessing, followed by feature learning using deep CNNs, meta-ensemble prediction, and finally, application-level classification. Initially, floor plan images and their corresponding architectural attributes are preprocessed through resizing, normalization, and data augmentation to ensure consistency and improve generalization. All floor-plan images were resized to 224 × 224 pixels and normalized to the range [0, 1]. Data augmentation was applied during training using random horizontal flipping, rotation, zooming, and width/height shifting to improve model generalization and reduce overfitting. No vertical flipping was applied. Complete augmentation parameters and implementation details are provided in Supplementary File 1. The processed data were then used to train multiple DL models for feature extraction and prediction. All base models and the CARE-MIRV-Net meta-learner were trained using a consistent configuration, including data augmentation, early stopping, and adaptive learning-rate scheduling. Early stopping monitored validation loss (val_loss) with a patience value of 5 epochs, and the best model weights were restored automatically after training. Adaptive learning-rate scheduling was implemented using the ReduceLROnPlateau callback, which monitored validation loss and reduced the learning rate by a factor of 0.3 after 2 epochs without improvement, with a minimum learning rate of 1 × 10⁻⁷. Complete implementation details and parameter settings are provided in Supplementary File 1. The dataset was randomly divided into training and testing sets using an 80:20 split ratio. A fixed random seed (42) was used to ensure reproducibility. Complete implementation details are provided in Supplementary File 1.

Figure 1. Workflow of the CARE-MIRV-Net framework for residential floor plan analysis. Schematic overview of the proposed protocol. The workflow includes (1) dataset acquisition and preprocessing, (2) feature learning and prediction using MobileNetV2, InceptionV3, ResNet101, and VGG16, (3) stacking-based meta-ensemble prediction using CARE-MIRV-Net, (4) rule-based residential layout classification, (5) SHapley Additive exPlanations (SHAP)-based interpretability analysis, and (6) performance evaluation. Please click here to view a larger version of this figure.

In the second stage, four pretrained CNNs, MobileNetV2, InceptionV3, ResNet101, and VGG16, are employed as base learners. Each model is fine-tuned using transfer learning to predict key architectural attributes, including square footage, number of bedrooms, bathrooms, and garages. For each transfer-learning model, the lower pretrained layers were kept frozen, while the upper layers and the custom regression head were fine-tuned during training. Detailed model-specific fine-tuning configurations are provided in Supplementary File 1. These models capture different aspects of spatial information: MobileNetV2 provides efficient feature extraction, InceptionV3 captures multiscale spatial patterns, ResNet101 learns deep hierarchical representations, and VGG16 ensures stable and consistent feature learning. The diversity among these models enhances the overall representational capability of the framework.

To enhance the accuracy and robustness of prediction further, a stacking-based meta-ensemble model, denoted as CARE-MIRV-Net, is proposed. At this step, predictions from all base models are pooled together to create a higher-dimensional feature space, which is input to a meta-learner. Base-model predictions are first generated independently using the trained MobileNetV2, InceptionV3, ResNet101, and VGG16 models. These predictions are then concatenated to form the meta-feature vector used by the meta-learner. To prevent data leakage, the training and testing datasets are strictly separated, and the meta-learner is trained only on predictions generated from the training data. The meta-model, which is a fully connected neural network, is trained to learn the optimal combination of base-model outputs to produce refined predictions. The meta-model consists of two fully connected layers with 512 and 256 neurons using ReLU activation, followed by dropout layers (0.4 and 0.3) and a linear output layer. The model is trained using the Adam optimizer (learning rate = 0.0001), a batch size of 32, and up to 15 epochs. Early stopping was implemented by monitoring validation loss (val_loss) with a patience value of 5 epochs. The minimum improvement criterion (min_delta) was set to the TensorFlow default value of 0, and the best model weights were restored automatically after training. Adaptive learning-rate scheduling was implemented using the ReduceLROnPlateau callback, which monitored validation loss and reduced the learning rate by a factor of 0.3 after 2 consecutive epochs without improvement. The minimum learning rate was set to 1 × 10⁻⁷, and the cooldown parameter used the default TensorFlow value of 0. This ensemble method reduces individual model biases and improves generalization through the complementary strengths of multiple architectures. Detailed implementation information is provided in Supplementary File 1.

After prediction, an interpretation layer is added to convert the numerical outputs to application-level decisions. Residential layouts can be classified into three classes based on the predictions of architectural characteristics: elderly care, healthcare-integrated (medical care), and general residential use. This classification is performed using predefined threshold-based decision rules based on the predicted square footage, number of bedrooms, and number of bathrooms. The category with the highest score is assigned as the final classification, whereas ties are resolved by selecting the category satisfying the greater number of decision criteria. The complete scoring rules and classification thresholds are provided in Supplementary File 2 (Algorithm 1). This action allows the framework to provide practical information for residential housing design.

The framework uses explainable AI with SHAP to provide transparency and interpretability. This component examines the contribution of the predictions from each base model to the output of the meta-model. SHAP analysis was performed using SHAP KernelExplainer (SHAP version 0.51.0) with 100 randomly selected training samples as the background dataset and 50 test samples for explanation generation. The complete SHAP configuration, including background sample selection and implementation settings, is provided in Supplementary File 1. The explainability layer helps to understand the decision-making process by measuring feature importance and displaying contribution patterns, thereby improving model transparency and reliability. The proposed framework is evaluated using MAE and R² across all target variables. For multioutput prediction, MAE was computed as the average absolute difference between actual and predicted values across all target variables, whereas R² was calculated by comparing the explained variance of the predictions with the corresponding ground-truth values for each output. The quantitative and qualitative analyses indicate that the proposed CARE-MIRV-Net improves prediction accuracy and supports rule-based residential layout classification. Classification reliability was assessed based on the accuracy of the underlying regression predictions and the consistency of the rule-based decision layer. The framework is a suitable and scalable solution for intelligent floor plan analysis and can be applied to smart housing, elderly-care planning, and healthcare-oriented residential design. A complete list of datasets, software packages, libraries, hardware resources, and computational tools used in this study is provided in the Table of Materials. Source code, environment configuration information, software dependency specifications, and implementation scripts required to reproduce the reported workflow are provided in Supplementary File 1.

Algorithm for the Protocol

The proposed framework takes a multistage approach to processing image-based floor plans of residential houses and producing predictive and decision-level outputs, as depicted in Algorithm 1 in Supplementary File 2. This process begins with data preprocessing, whereby the input images are resized to a standard resolution and normalized to provide uniformity throughout the dataset. All floor-plan images were resized to 224 × 224 pixels and normalized to the range [0, 1]. This step optimizes the images for learning by deep neural networks and enhances overall model convergence. The second phase involves using several DL models as base learners, including MobileNetV2, InceptionV3, ResNet101, and VGG16. Independently, each model takes the input images and estimates important architectural features such as square footage and the number of bedrooms, bathrooms, and garages. The dataset was randomly divided into training and testing sets using an 80:20 split ratio, and a fixed random seed was used to ensure reproducibility. These models capture different spatial properties of the floor plans and provide complementary predictions. The feature representation is then constructed by stacking the outputs of each base model to create a single feature representation. This joint output is subsequently fed into a meta-ensemble model, CARE-MIRV-Net, which is trained to combine the predictions of all base models. Base-model predictions were generated independently and concatenated to form the meta-feature vector. Data leakage was prevented through strict separation of the training and testing datasets. The meta-model refines these predictions and produces the final architectural attribute forecasts. The meta-model consisted of fully connected layers with 512 and 256 neurons, ReLU activation functions, dropout rates of 0.4 and 0.3, the Adam optimizer (learning rate = 0.0001), a batch size of 32, and up to 15 training epochs with early stopping. After prediction, a decision layer is used to contextualize the results. According to the estimated values, each residential layout is categorized as elderly care, medical care, or general residential use. Residential categories were assigned using predefined threshold-based scoring rules derived from the predicted floor-plan attributes. The category with the highest score was selected as the final classification. Complete classification thresholds and decision rules are provided in Algorithm 1 (Supplementary File 2). Lastly, the framework incorporates an explainability component based on SHAP to assess the impact of each base model on the final prediction. SHAP analysis was performed as described previously and in Supplementary File 1. This component increases transparency and aids in understanding the decision-making process. The effectiveness and viability of the proposed approach were assessed using standard regression performance measures. Performance was evaluated using a single experimental run with a fixed random seed.

Evaluation Models

In this section, the DL models used in the study are presented, including both the individual base models and the proposed CARE-MIRV-Net. The benchmark models were selected to represent diverse and widely adopted DL architectures. MobileNetV2 was included as a lightweight and computationally efficient network, and InceptionV3 was selected for its ability to capture multiscale spatial features. ResNet101 was chosen due to its deep residual learning capability for hierarchical feature extraction, and VGG16 was included as a well-established convolutional architecture. All benchmark models were trained using identical preprocessing procedures, data augmentation settings, dataset partitions, optimization parameters, batch size, and training configuration to ensure a fair comparative evaluation.

MobileNetV2:

MobileNetV2 is a small and compact CNN system that is engineered to be fast, high-performing, and with less computational complexity. It presents some of the major innovations like inverted residual connections and linear bottlenecks, which facilitate efficient feature extraction and still have a high representational power. MobileNetV2 can use depthwise separable convolutions to greatly minimize the total number of parameters and the computational cost of traditional convolutional networks, and it is thus well adapted to large image-based learning problems.

MobileNetV2 is used in the suggested framework as one of the foundation DL models to predict the architectural attributes of residential floor plan images. The model is able to learn spatial patterns like room distribution, layout density, and structural organization that occur in floor plans effectively because of its efficient design. Such learned representations play an important role in estimating important features such as square footage, number of bedrooms, bathrooms, and garages accurately. MobileNetV2 is a lightweight model, which is a good candidate to be included in the proposed meta-ensemble framework. It adds some complementary feature representations to other DL models, and it enhances the overall robustness and generalization of the system. This model learning is very important in terms of improving the accuracy of predictions and in assisting in the classification of residential designs based on healthcare.

The MobileNetV2 model is trained using a transfer-learning strategy with pretrained weights from the ImageNet dataset. The input images are scaled to a fixed size of 224 × 224 × 3, which is compatible with the architecture. The images were resized to 224 × 224 pixels, normalized to the range [0, 1], and augmented using random flipping, rotation, zooming, and shifting. A batch size of 32 was used during training. The initial classification head of MobileNetV2 is removed and replaced with a regression head. The regression head consisted of a Global Average Pooling layer followed by fully connected layers with 512 and 256 neurons, batch normalization, dropout layers (0.4 and 0.3), and a four-neuron linear output layer. To facilitate domain adaptation, the lower pretrained layers were frozen, whereas the upper layers beginning from block_13_expand and the custom regression head were fine-tuned. This selective training approach enables the model to retain general visual features while learning domain-specific spatial attributes of floor plan images. The extracted feature maps are processed through a Global Average Pooling layer and fully connected layers with 512 and 256 neurons using ReLU activation. To enhance generalization and reduce overfitting, batch normalization and dropout layers (0.4 and 0.3) are incorporated. The final output layer contains four neurons with linear activation corresponding to the predicted architectural attributes. The Adam optimizer was used with a learning rate of 0.0001 and TensorFlow/Keras default parameters (β₁ = 0.9, β₂ = 0.999, ε = 1 × 10⁻⁷, amsgrad = False). For multioutput prediction, MAE and R² were computed by comparing the predicted and actual values for each target variable and then averaging the results across all outputs. Early stopping was applied based on validation loss, and adaptive learning-rate reduction was used to improve convergence. Training was performed for up to 15 epochs.

InceptionV3:

InceptionV3 is a deep CNN architecture that is capable of capturing multiscale features with the use of parallel convolutional operations. It is founded on the Inception module, which uses a variety of filter sizes in the same layer to both extract fine-grained and coarse features at the same time. This type of architecture design allows the network to learn intricate spatial hierarchies and be computationally efficient. On top of this, InceptionV3 uses factorized convolutions and dimensionality reduction, which enhance performance and minimize computational cost.

InceptionV3 is applied in the proposed framework as an effective feature extractor to analyze residential floor plan images. Floor plans consist of a variety of spatial patterns, such as room sizes, structural plans, and connectivity, necessitating multiscale feature learning. Inception architecture is especially better suited to this task by the fact that it is able to represent local specifics (e.g., small rooms) and global structures (e.g., the organization of the overall layout) at the same time. InceptionV3 provides complementary representations to other models like MobileNetV2 in the context of the meta-ensemble framework. The diversity of predictions is increased by its capability to model complex spatial relationships, and this is important in enhancing ensemble performance. This eventually assists in better prediction of architectural characteristics and reinforces downstream classification of elderly-friendly and healthcare-integrated housing design.

The InceptionV3 model is trained using a transfer-learning approach with pretrained weights from the ImageNet dataset. The input images are scaled to 224 × 224 × 3 to ensure compatibility with the network architecture and consistency across all models within the framework. The images were resized to 224 × 224 pixels, normalized to the range [0, 1], and augmented using random flipping, rotation, zooming, and shifting. A batch size of 32 was used during training.

The original classification layers of InceptionV3 are removed, and a custom regression head is introduced to enable multioutput prediction. The custom regression head consisted of a Global Average Pooling layer followed by fully connected layers with 512 and 256 neurons, batch normalization, dropout layers (0.4 and 0.3), and a four-neuron linear output layer. The convolutional base is partially fine-tuned, with the lower pretrained layers frozen and the upper layers together with the custom regression head fine-tuned to learn domain-specific floor plan features while retaining general visual representations acquired during pretraining. After the convolutional backbone, a Global Average Pooling layer transforms the feature maps into a compact feature representation. This is followed by fully connected layers containing 512 and 256 neurons with ReLU activation functions. To minimize overfitting and improve generalization, dropout rates of 0.4 and 0.3 are applied. The final output layer consists of four neurons with linear activation corresponding to the prediction of square footage, number of bedrooms, number of bathrooms, and number of garages. The model is trained using the Adam optimizer with a learning rate of 0.0001, a batch size of 32, and up to 15 training epochs with early stopping and adaptive learning-rate reduction to ensure stable convergence.

ResNet101:

ResNet101 is a deep structure of the CNN that is constructed on the principle of residual learning that makes it possible to train extremely deep networks by overcoming the vanishing gradient problem. It adds residual connections, or so-called skip connections, which enable the network to learn identity mappings and to retain information across layers. This architecture greatly enhances the stability of training and allows the model to learn intricate hierarchical characteristics. ResNet101 has a deep representational capacity of 101 layers and is, therefore, very effective in tasks that require extra spatial features.

ResNet101 will be used in the proposed framework as a high-capacity feature extractor, which will analyze the images of residential floor plans. The model can be used to represent complex spatial relationships, including room connectivity, layout complexity, and structural variations, because of its deep architecture. These features are critical in making precise predictions on architectural features such as square footage, bedrooms, bathrooms, and garages. In the meta-ensemble design, ResNet101 is a significant component and provides deep and abstract feature representations. ResNet101 is more concerned with fine-grained structural features and thus more diverse among base learners compared to lightweight models such as MobileNetV2. This heterogeneity plays an essential role in enhancing the performance of an ensemble and robust predictions, especially when dealing with healthcare-based residential classification.

ResNet101 is trained using a transfer-learning approach with pretrained weights from the ImageNet dataset. The input images are scaled to 224 × 224 × 3, which ensures architectural compatibility and consistency across all models within the framework. The images were resized to 224 × 224 pixels, normalized to the range [0, 1], and augmented using random flipping, rotation, zooming, and shifting. A batch size of 32 was used during training. The original classification head of ResNet101 is removed (include_top=False), and a custom regression head is introduced to enable multioutput prediction. The regression head consisted of a Global Average Pooling layer followed by fully connected layers with 512 and 256 neurons, batch normalization, dropout layers (0.4 and 0.3), and a four-neuron linear output layer. To balance feature reuse and domain adaptation, the lower pretrained layers were frozen, whereas the deeper layers beginning from conv4_block1_1_conv and the custom regression head were fine-tuned. This selective training strategy enables the model to retain generic visual features while adapting higher-level representations to floor plan images. The output of the convolutional base is passed through a Global Average Pooling layer to generate a compact feature representation. This is followed by fully connected layers with 512 and 256 neurons using ReLU activation functions. Batch normalization is applied to stabilize learning, and dropout layers with rates of 0.4 and 0.3 are incorporated to reduce overfitting and improve generalization. The final output layer contains four neurons with linear activation corresponding to square footage, number of bedrooms, bathrooms, and garages. The Adam optimizer was used with a learning rate of 0.0001, as described in the training configuration. Data augmentation included random flipping, rotation, zooming, and shifting to improve model generalization. Training was performed for up to 15 epochs with early stopping based on validation loss and adaptive learning-rate reduction.

VGG16:

VGG16 is a simple neural network architecture of the deep convolutional type that is effective and simple in visual recognition tasks. It is made up of 16 layers with a regular or homogeneous design incorporating small 3 × 3 convolutional filters, which facilitate learning hierarchical feature representations by the network. The architecture is depth-focused and simplistic, which means it is capable of capturing low-level details like edges and textures and high-level semantic structures. VGG16, with its regular design and good performance, is now a popular choice of backbone when performing transfer learning in image-related tasks.

In the suggested model, VGG16 is used as a baseline DL model to derive spatial features of images of residential floor plans. The fact that it is organized enables it to be very useful in capturing the finer details of layout patterns like room boundaries, structural alignments, and spatial organization. The features are critical in effectively forecasting the attributes of architecture such as square feet, bedrooms, bathrooms, and garages. In the meta-ensemble system, VGG16 provides stable and well-generalized feature representations. VGG16 offers a more balanced feature extraction approach compared to deeper architectures, such as ResNet101, and multiscale architectures, such as InceptionV3, increasing base learner diversity. This heterogeneity is critical in enhancing the performance of the ensemble and reliable forecasts of healthcare-oriented residential classification.

The VGG16 model is trained using a transfer-learning approach with pretrained weights from the ImageNet dataset. The input images are scaled to 224 × 224 × 3 to ensure compatibility with the architecture and consistency across all models within the framework. The images were resized to 224 × 224 pixels, normalized to the range [0, 1], and augmented using random flipping, rotation, zooming, and shifting. A batch size of 32 was used during training. The original classification layers are removed (include_top=False), and a custom regression head is added to adapt the model for multioutput prediction. The regression head consisted of a Global Average Pooling layer followed by fully connected layers with 512 and 256 neurons, batch normalization, dropout layers (0.4 and 0.3), and a four-neuron linear output layer. The lower pretrained layers were frozen, whereas the layers beginning from block4_conv1 and the custom regression head were fine-tuned. This strategy enables the model to retain general visual characteristics while learning domain-specific representations relevant to floor plan analysis. The output features of the convolutional base are passed through a Global Average Pooling layer to generate a compact feature vector. This is followed by fully connected layers with 512 and 256 neurons using ReLU activation functions. Batch normalization and dropout layers with rates of 0.4 and 0.3 are incorporated to stabilize training, reduce overfitting, and improve generalization. The final output layer contains four neurons with linear activation corresponding to square footage, number of bedrooms, bathrooms, and garage predictions. The Adam optimizer was used with a learning rate of 0.0001, as described in the training configuration. Data augmentation included random flipping, rotation, zooming, and shifting to improve model generalization. Training was performed for up to 15 epochs with early stopping based on validation loss and adaptive learning-rate reduction.

CARE-MIRV-Net (Proposed Meta-Ensemble DL Model):

The suggested CARE-MIRV-Net (Context-Aware Residential Ensemble utilising MobileNetV2, InceptionV3, ResNet101, and VGG16 Network) is a stacking-based meta-ensemble DL framework that is aimed at improving the estimate of architectural features on the basis of residential floor plan pictures. The architecture combines various heterogeneous CNNs by exploiting their synergistic advantages in the extraction of features. The proposed model is based on the combination of lightweight, deep, and multiscale architecture that enhances predictive performance and provides robustness in a wide range of residential layouts. In contrast to traditional single-model-based techniques, CARE-MIRV-Net follows a hierarchical learning approach, where base models provide initial predictions, which are then improved by a meta-learner.

When it comes to residential design that is spatially conscious and health-conscious, the proper interpretation of floor plan layouts is of paramount importance. Single DL models tend to be insufficient in generalizing different architectural patterns. The proposed framework will overcome this weakness by using four different architectures: MobileNetV2, InceptionV3, ResNet101, and VGG16, which bring forth different representational abilities of their own. MobileNetV2 can be used to extract features efficiently and is lightweight; InceptionV3 learns spatial patterns across multiple scales; ResNet101 learns deep hierarchical representations; and VGG16 learns features consistently and reliably. A combination of these models facilitates the framework to embrace both the local and global spatial attributes, which enhances the accuracy and reliability of the predicted features like square footage, number of bedrooms, bathrooms, and garages. The above predictions can also be used to predict further classification of residential layouts to include elderly-friendly, healthcare-integrated, and general residential.

CARE-MIRV-Net is implemented using a two-stage stacking-based ensemble strategy. In the first stage, the four base models independently generate predictions for the target variables. Meta-learner inputs were generated by concatenating the prediction outputs produced by the trained base models on the training data. These concatenated predictions formed the 16-dimensional meta-feature vectors used for meta-model training. Information leakage was prevented through strict separation of the training and testing datasets, and no testing samples were used during either base-model or meta-model training. The same training and validation datasets used for the base models were employed during the stacking process. During both training and inference, the 16-dimensional meta-feature vector was constructed by concatenating the four prediction outputs generated by MobileNetV2, InceptionV3, ResNet101, and VGG16. Since each model generates a four-dimensional output vector, the concatenated representation produces a 16-dimensional input to the meta-learner. This transformation combines the predictions from all base models and serves as the input to the second stage of the framework. The second stage involves constructing a fully connected neural network meta-learner to learn the optimal combination of base-model predictions. The meta-learner consisted of fully connected layers with 512 and 256 neurons, ReLU activation, batch normalization, dropout rates of 0.4 and 0.3, and a four-neuron linear output layer. Dropout layers with rates of 0.4 and 0.3 were applied after the dense layers to reduce overfitting and improve generalization. The meta-model was trained using the Adam optimizer with a learning rate of 0.0001, a batch size of 32, and up to 15 epochs with early stopping and adaptive learning-rate reduction. Validation data were used to monitor model performance during training and to select the best-performing model. After training, the meta-ensemble model generated final predictions by combining outputs from all base learners. MAE and R² were used to evaluate the performance of CARE-MIRV-Net. For multioutput prediction, MAE and R² were computed by comparing the predicted and actual values for each target variable and then averaging the results across all outputs. Results were obtained from a single experimental run using a fixed random seed. The proposed framework improves predictive ability by combining several DL structures through a stacking mechanism and providing interpretable analysis of residential floor plans. This renders CARE-MIRV-Net applicable in the context of geriatric-focused and healthcare-oriented residential design. The source code, environment configuration information, software dependency specifications, and implementation documentation are provided in Supplementary File 1.

Performance Evaluation

Performance analysis of the proposed framework is carried out to determine its predictive power, robustness, and generalization performance across different architectural characteristics. Quantitative and qualitative analyses are conducted to confirm the usefulness of the proposed model. The assessment procedure involves both standard regression measures and controlled experimental environments to enable fair comparison with the baseline models. Experiments were conducted using a single experimental run with a fixed random seed to ensure reproducibility and consistency across all benchmark and proposed models.

Performance Parameters:

Two well-known regression measures, MAE and R², are used to assess the performance of the proposed framework. MAE measures the average magnitude of prediction errors and provides a clear indication of the proximity between predicted and actual values. Conversely, the R² value is used to assess the proportion of variance in the target variables explained by the model, reflecting its overall predictive power. A combination of these metrics provides a comprehensive evaluation of the accuracy and generalization performance of all predicted architectural features. For multioutput prediction, MAE and R² were computed by comparing the predicted and actual values for each target variable and then averaging the results across all outputs.

Experimental Setup:

The experiments were conducted in a cloud-computing environment using Python (version 3.12.12). The proposed protocol was implemented using TensorFlow (version 2.19.0), Keras (version 3.13.2), NumPy (version 2.4.6), Pandas (version 2.3.3), Scikit-learn (version 1.6.1), Matplotlib (version 3.9), and SHAP (version 0.51.0). The computational environment utilized an Intel Xeon processor operating at 2.20 GHz with approximately 31 GB of system memory. The experiments were conducted on an Ubuntu 22.04 LTS operating system utilizing NVIDIA Tesla T4 × 2 GPUs. The dataset contains 2,640 residential floor plan images that were preprocessed and subdivided into training and testing datasets. The dataset was divided using an 80:20 split ratio, resulting in 2,112 training samples and 528 testing samples. MobileNetV2, InceptionV3, ResNet101, and VGG16 are four DL models that were trained using a fine-tuning approach with transfer learning. A stacking-based meta-ensemble model, CARE-MIRV-Net, was then constructed to combine predictions from these base models. Each model was trained under uniform conditions, including image resizing to 224 × 224 pixels, data augmentation, and early stopping to reduce overfitting. Data augmentation included random flipping, rotation, zooming, and shifting. A batch size of 32 and a maximum of 15 training epochs were used, with early stopping based on validation loss and adaptive learning-rate reduction. The final analysis was carried out on a hidden test set to provide an unbiased and reliable assessment of performance. The hidden test set was generated during the initial dataset partitioning and remained completely isolated throughout model training and development to ensure unbiased performance evaluation.

Dataset Description:

The dataset used to support the findings of this study is publicly available on the Kaggle platform with the title “Floor Plan Images and Their Details”²⁸. The dataset is available at: https://www.kaggle.com/datasets/adilmohammed/floor-plan-images-and-their-details (accessed April 16, 2026). The data consist of 2,640 residential floor plan images and organized architectural metadata. Every sample is a distinct housing plan, which provides a wide range of residential layouts with different sizes, configurations, and spatial arrangements. The dataset size is sufficient for DL-based training and contains a variety of architectural patterns. The dataset consists of a two-dimensional floor plan image and related numerical attributes describing the structural characteristics of the residence. These features include total square footage, number of bedrooms, number of bathrooms, and number of garages. Moreover, every record contains an image path, which allows direct mapping of visual inputs to labels. The dataset variables include floor plan image, image path, square footage, number of bedrooms, number of bathrooms, and number of garages. The floor plan image serves as the input feature, while the architectural attributes are used as prediction targets. The dataset is therefore suitable for supervised learning, with images serving as input features and architectural attributes serving as regression targets.

The dataset contains a broad variety of residential layouts, ranging from compact layouts with fewer rooms to large multiroom layouts. This variability enables the learning of meaningful spatial representations, including room distribution, layout density, and structural complexity. This diversity is especially important for the proposed framework, as it supports the categorization of floor plans into elderly-friendly, healthcare-integrated, and general residential layouts. Before model training, the dataset is processed through a sequence of preprocessing procedures to ensure consistency and compatibility with DL models. All images were resized to 224 × 224 pixels and normalized to the range [0, 1] before training. The dataset is then organized by matching image paths with their corresponding metadata, after which it is separated into training and testing subsets to facilitate performance analysis. The dataset was randomly divided into training and testing sets using an 80:20 split ratio. Records containing complete image and metadata information were included in the analysis, whereas incomplete or invalid records were excluded. A fixed random seed was used during dataset partitioning to ensure reproducibility. The dataset provides a detailed basis for AI-driven analysis of residential floor plans. The combination of 2,640 annotated images and diverse architectural attributes supports multioutput regression modeling and enables the integration of spatial intelligence with healthcare-oriented residential design decision-making.

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