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Related Experiment Video

Updated: Jun 17, 2025

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Published on: March 19, 2017

Inferring building height from footprint morphology data.

Clinton Stipek¹, Taylor Hauser², Daniel Adams²

¹Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA. stipekcw@ornl.gov.

Scientific Reports

|August 12, 2024

Summary

This summary is machine-generated.

Accurately inferring building height is now possible using only footprint morphology data. This method achieves 95% accuracy within 3m, offering a scalable solution for global built environment analysis.

Keywords:

Building height Built environment Machine learning Urban planning XGBoost

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Area of Science:

Urban planning and remote sensing.
Geospatial analysis and built environment characterization.

Background:

Global urban growth necessitates accurate built environment data for planning and impact assessment.
Existing building height models face limitations in scale and data requirements.

Purpose of the Study:

To develop and validate a novel method for inferring building height globally.
To assess the accuracy of predicting building height using only footprint morphology.

Main Methods:

Experiments conducted on 74.55 million buildings across the United States, France, and Germany.
Utilized building footprint morphology data to infer height without ancillary data.

Main Results:

Achieved 95% accuracy in predicting building height within 3 meters of the true height.
Demonstrated the efficacy of using footprint morphology alone for height prediction.

Conclusions:

Building height can be accurately inferred from footprint data independently.
This method offers a scalable and data-efficient approach for global building height modeling.