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Updated: Apr 23, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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Image-based prediction of residential building attributes with deep learning.

Weimin Huang1, Alexander W Olson2, Elias B Khalil1

  • 1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON Canada.

Journal of Industrial Ecology
|April 22, 2026
PubMed
Summary

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Machine learning accurately estimates building floor area and age from street-view images. This advances urban metabolism studies by providing crucial data for material flow and greenhouse gas analysis.

Area of Science:

  • Urban planning and remote sensing
  • Environmental science and sustainability
  • Computer vision and machine learning

Background:

  • Building age and floor area are critical for understanding urban metabolism, material flows, and embodied greenhouse gas (GHG) emissions.
  • Traditional methods for collecting this data are often unreliable, uneven, and costly.
  • Accurate building data is essential for effective resource management and environmental impact assessment in urban areas.

Purpose of the Study:

  • To develop and evaluate an image-based machine learning approach for estimating building floor area and age.
  • To address the limitations of traditional survey methods in data acquisition for urban studies.
  • To provide a scalable solution for generating building attribute data for large-scale analyses.

Main Methods:

Keywords:
Google Street Viewbuilding attribute estimationindustrial ecologymachine learningmaterial stocksurban sustainability

Related Experiment Videos

Last Updated: Apr 23, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

11.0K
  • Building attributes (floor area and age) were estimated using machine learning models trained on Google Street View images.
  • Area prediction was formulated as a regression problem, and age prediction as a classification problem across six historical periods.
  • An EfficientNetV2 module was employed for feature extraction, followed by fully connected layers for attribute estimation.

Main Results:

  • The model achieved a mean absolute percentage error of 19.42% for floor area prediction and 70.27% accuracy for age prediction in Toronto.
  • Performance varied across five other Canadian cities, demonstrating the importance of local training data.
  • The study confirmed the feasibility of using street-view imagery for automated building attribute estimation.

Conclusions:

  • Machine learning offers a viable and automated method for estimating key building attributes from readily available street-view imagery.
  • This approach can significantly improve the availability and accuracy of data for urban metabolism, material flow, and embodied GHG studies.
  • The findings support the development of large-scale, data-driven analyses for sustainable urban development and environmental management.