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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

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An OpenStreetMap derived building classification dataset for the United States.

Henrique F de Arruda¹, Sandro M Reia², Shiyang Ruan²

¹Geography and Geoinformation Science, College of Science, George Mason University, 4400 University Dr., Fairfax, 22030, VA, USA. h.f.arruda@gmail.com.

Scientific Data

|November 9, 2024

Summary

This study created a nationwide dataset classifying over 67 million buildings as residential or non-residential. This valuable resource aids urban planning and population estimation.

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Area of Science:

Geospatial science
Urban informatics
Data science

Background:

Building classification data is essential for urban planning, population estimation, and emergency response.
Readily available, comprehensive building type data for the entire United States is lacking.
Existing datasets often lack the scale or detail required for nationwide applications.

Purpose of the Study:

To develop a comprehensive dataset classifying residential and non-residential buildings across the entire United States.
To provide a valuable resource for urban planners, transportation planners, and researchers.
To improve the availability of crucial building type information for various applications.

Main Methods:

Utilized building footprints and OpenStreetMap data for classification.
Developed a methodology for classifying millions of building structures.
Validated the dataset using authoritative ground truth data in select U.S. counties.

Main Results:

Generated a dataset classifying 67,705,475 buildings nationwide.
Achieved high precision in non-residential building classification.
Demonstrated high recall for residential building classification.

Conclusions:

The developed dataset significantly enhances the availability of building type information for the U.S.
The classification methodology proves effective for large-scale geospatial data analysis.
This resource is expected to benefit urban planning, traffic analysis, and emergency response efforts.