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Related Experiment Video

Updated: Jul 2, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

An interpretable attention-based TabTransformer framework with feature fusion for green architecture classification.

Zhao Yiyuan1, Li Chang2, Li Xintong2

  • 1Xi'an University of Architecture and Technology Huaqing College, Xi'an, 710054, Shannxi, China. zhaoyiyuan_1019@xauat.edu.cn.

Scientific Reports
|June 19, 2026
PubMed
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This summary is machine-generated.

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This study introduces EcoArch-TabFusionNet, a deep learning model for automated green architecture classification. It achieves 97.5% accuracy, enhancing sustainability assessments and reducing manual efforts in building evaluations.

Area of Science:

  • Artificial Intelligence
  • Sustainable Architecture
  • Environmental Science

Background:

  • Traditional green building assessments are manual, costly, and time-consuming.
  • Scalable, data-driven methods are needed to analyze environmental effects of architecture.
  • Existing automated approaches have limitations in fusing diverse architectural data.

Purpose of the Study:

  • To develop a deep learning framework for automated classification of green vs. non-green architecture.
  • To enhance sustainability assessments through scalable, data-driven analysis.
  • To minimize reliance on manual and expensive certification methods.

Main Methods:

  • Proposed EcoArch-TabFusionNet, a Tab transformer-based Fusion Network.
  • Fused architectural, energy, and contextual attributes using attention-based TabTransformer.
Keywords:
Artificial intelligenceData acquisitionDeep learningExpert systemsGreen designSustainable environment

Related Experiment Videos

Last Updated: Jul 2, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
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Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

  • Integrated feature domains: Energy, Eco-Tech, Design, and Context.
  • Employed Principal Component Analysis (PCA) for dimensionality reduction.
  • Incorporated explainable AI (XAI) components (attention heatmaps, LIME, SHAP) for transparency.

    • Main Results:

    • Achieved a superior classification accuracy of 97.5%.
    • Outperformed four tabular transformer baselines.
    • Demonstrated effective fusion of diverse architectural features.
    • Provided transparent decision logic through XAI.

    Conclusions:

    • EcoArch-TabFusionNet offers a highly accurate and scalable solution for green architecture classification.
    • The framework enhances sustainability analysis by providing data-driven insights.
    • Explainable AI components ensure transparency and trust in the automated assessment process.