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URBAN-AI uses artificial intelligence and street-level images to identify building materials and facade features. This AI workflow offers crucial data for urban sustainability and circular economy initiatives globally.

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Area of Science:

  • Urban Planning and Design
  • Artificial Intelligence
  • Computer Vision

Background:

  • Reliable building-level data is essential for urban sustainability and circular economy practices but is often lacking.
  • Existing data collection methods can be time-consuming, costly, and limited in scope, particularly in data-scarce regions.

Purpose of the Study:

  • To introduce URBAN-AI, a novel workflow utilizing multimodal artificial intelligence to extract building material and facade characteristics from street-level imagery.
  • To evaluate the accuracy, coverage, and cost-efficiency of the URBAN-AI workflow across diverse urban contexts, from high-income to low-income settings.
  • To generate image-based indicators supporting various downstream applications in urban planning and sustainability.

Main Methods:

  • Development of the URBAN-AI workflow employing multimodal artificial intelligence for image analysis.
  • Application of the workflow across six cities (Zurich, San Francisco, Melbourne, Mumbai, Cape Town, Rio de Janeiro).
  • Analysis and verification of outputs using 9,056 high-confidence images, with a focus on facade material classification using vision-language models.

Main Results:

  • Achieved a mean composite module accuracy of 87.7% across all evaluated cities.
  • Successfully inferred building typology, material type, condition, and architectural style, even in low-income and data-scarce areas.
  • Generated valuable image-based indicators for historical facades, retrofitting suitability (seismic, energy), urban morphology, facade greening, and flood exposure.

Conclusions:

  • The URBAN-AI workflow demonstrates significant potential for providing essential building data to support urban sustainability and circular economy goals.
  • The approach offers a cost-effective and scalable alternative to traditional data collection methods, especially in data-scarce environments.
  • The study contributes open resources, including the Global Building Facade Dataset and the URBAN-AI workflow, fostering further research and application.