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Masonry, known for its strength, durability, and aesthetic versatility, encompasses construction with solid stone or man-made units like bricks, clay tiles, terra cotta, and concrete blocks, combined to form structures like walls, floors, and arches. These units are placed in a systematic fashion, known as coursing, and are bound together using mortar—a mixture typically made of water, cement, and sand.
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Urban fabric decoded: High-precision building material identification via deep learning and remote sensing.

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We developed an automated system using AI and street view imagery to precisely identify building materials for better carbon reduction and retrofitting strategies. This scalable approach aids sustainable urban planning and energy efficiency improvements.

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Area of Science:

  • Urban Planning and Sustainability
  • Artificial Intelligence in Environmental Science
  • Remote Sensing and Geospatial Analysis

Background:

  • Accurate building material data is crucial for embodied carbon reduction, retrofitting, and urban circularity.
  • Current material databases are often project-specific or geographically limited, providing only approximate data.
  • Inadequate records and cost constraints hinder the acquisition of large-scale, precise material information.

Purpose of the Study:

  • To introduce a novel automated framework for identifying building materials using remote sensing and Google Street View imagery.
  • To demonstrate the scalability and adaptability of the framework across diverse Danish urban landscapes.
  • To provide high-resolution insights into material distribution for sustainable urban development.

Main Methods:

  • Utilized a deep learning model trained on sensing technology and Google Street View data.
  • Initial model training and validation were performed using a comprehensive dataset from Odense.
  • The framework was extended to characterize building materials in Copenhagen, Aarhus, and Aalborg.

Main Results:

  • Successfully identified roof and facade materials across multiple Danish cities with high resolution.
  • Demonstrated the model's scalability and adaptability to varied geographic contexts and architectural styles.
  • Provided detailed insights into the distribution of building materials across different urban settings.

Conclusions:

  • The automated framework offers a scalable and adaptable solution for precise building material identification.
  • Findings are pivotal for informing sustainable urban planning and revising building codes for emission reduction.
  • Optimized retrofitting efforts can be achieved by leveraging these high-resolution material distribution insights.