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High resolution building facade dataset for facade elements parsing.

Shuchang Xu1, Fan Wu1, Junjie Cheng1

  • 1School of Information Science and Technology, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.

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Summary
This summary is machine-generated.

A new high-resolution building facade dataset, hznu_facade, offers extensive data for urban modeling and digital twin construction. It features detailed annotations, especially for windows, advancing architectural element recognition.

Keywords:
ArchitectureComputer visionFacade parsing

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Area of Science:

  • Computer Vision
  • Urban Planning
  • Digital Architecture

Background:

  • Urban modeling and digital twin cities require high-resolution facade data.
  • Existing datasets lack comprehensive annotations for architectural elements, particularly windows.
  • Diverse architectural styles and lighting conditions are crucial for robust facade analysis.

Purpose of the Study:

  • Introduce the hznu_facade dataset for urban modeling and digital twin construction.
  • Provide a rich resource for facade analysis and architectural element recognition.
  • Establish a benchmark for facade parsing algorithms.

Main Methods:

  • Collected facade images of 624 buildings in Hangzhou, China.
  • Created twin versions of images with perspective correction.
  • Annotated six categories (buildings, cars, trees, windows, skies, doors) using segmentation models, focusing on high-density window data (43,277 windows).

Main Results:

  • The hznu_facade dataset contains 624 building facade images with perspective correction.
  • Features high-density annotations for 43,277 windows, exceeding existing datasets.
  • Includes images under diverse lighting conditions, enhancing data utility.

Conclusions:

  • The hznu_facade dataset significantly advances building facade analysis and architectural element recognition.
  • It serves as a valuable benchmark for facade parsing algorithms.
  • The dataset supports applications in urban modeling, digital twins, and virtual reality.