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Learning from urban form to predict building heights.

Nikola Milojevic-Dupont1,2, Nicolai Hans3, Lynn H Kaack4

  • 1Chair of Sustainability Economics, School of Planning, Building and Environment, Technische Universität Berlin, Berlin, Germany.

Plos One
|December 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning model to predict building heights using open geospatial data. The method offers a cost-effective solution for urban planning, especially in areas lacking detailed 3D building models.

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Area of Science:

  • Urban planning and remote sensing
  • Geospatial data analysis
  • Machine learning applications in urban studies

Background:

  • Sustainable urban planning requires high-resolution building stock data for effective policy implementation.
  • Existing 3D building models are costly to create and maintain, posing challenges for many cities.
  • There is a need for cost-effective methods to estimate building characteristics where detailed data is unavailable.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting building heights.
  • To utilize open-access geospatial data on urban form for height prediction.
  • To assess the model's performance in regions without existing 3D data and evaluate the impact of citizen-contributed data.

Main Methods:

  • A machine learning approach was employed to predict building heights.
  • The model was trained using open-access geospatial data, including building footprints and street networks.
  • Model performance was evaluated using data from four European countries and tested in Brandenburg, Germany.

Main Results:

  • Urban fabric morphology was found to be highly predictive of building height.
  • The model achieved an average prediction error well below typical floor height (approx. 2.5m) in Brandenburg.
  • Even limited citizen-collected local height data significantly improved prediction accuracy.

Conclusions:

  • The developed method enables reliable and cost-effective prediction of building heights using readily available geospatial data.
  • This approach facilitates the estimation of missing urban infrastructure data, supporting climate change mitigation strategies.
  • Open government data and volunteered geographic information are valuable resources for scalable scientific applications in urban studies.