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From Footprints to Functions: A Comprehensive Global and Semantic Building Footprint Dataset.

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A new global building dataset with 2.7 billion footprints was created by combining AI and crowd-sourced data. This comprehensive dataset aids in urban planning, crisis management, and natural hazard risk assessment.

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Area of Science:

  • Geographic Information Science
  • Urban Planning
  • Disaster Risk Reduction

Background:

  • Buildings are crucial for settlement pattern analysis, urban planning, and multi-hazard risk assessment.
  • Accessible global building data is essential for various applications, including crisis management and energy efficiency.
  • Existing datasets often have completeness biases, particularly in regions with lower human development index.

Purpose of the Study:

  • To create the most detailed and extensive global building footprint dataset to date.
  • To address the need for accessible and comprehensive building data for research and practical applications.
  • To provide a reliable foundation for natural hazard vulnerability assessment and population distribution modeling.

Main Methods:

  • Conflation of AI-derived datasets (Google Open Buildings, Microsoft Global ML Building Footprints) with crowd-sourced data (OpenStreetMap).
  • Classification of building footprints using the Global Earthquake Model building taxonomy.
  • Validation of occupancy types and building height estimations using Kullback-Leibler divergence and cadaster data from Slovenia and Greece.

Main Results:

  • A dataset of 2.7 billion building footprints, offering unprecedented detail and global coverage.
  • Improved data completeness by balancing biases present in individual datasets.
  • Demonstrated reliability and value of the dataset for global-scale building information, despite minor misclassifications.

Conclusions:

  • The conflated global building dataset provides a valuable resource for diverse applications.
  • The dataset enhances capabilities in natural hazard assessment, urban planning, and population distribution modeling.
  • Further research can leverage this dataset to refine risk assessments and inform policy decisions.