MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 - 2015)

Johannes H Uhl; Stefan Leyk

doi:10.1016/j.dib.2022.108369

MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 - 2015)

Data Brief. 2022 Jun 13:43:108369. doi: 10.1016/j.dib.2022.108369. eCollection 2022 Aug.

Authors

Johannes H Uhl^{1

2

3}, Stefan Leyk^{2

3}

Affiliations

¹ University of Colorado Boulder, Cooperative Institute for Research in Environmental Sciences (CIRES) 216 UCB, Boulder, CO-80309, USA.
² University of Colorado Boulder, Institute of Behavioral Science, 483 UCB, Boulder, CO-80309, USA.
³ University of Colorado Boulder, Department of Geography, 260 UCB, Boulder, CO-80309, USA.

Abstract

Despite abundant data on the spatial distribution of contemporary human settlements, historical datasets on the long-term evolution of human settlements at fine spatial and temporal granularity are scarce, limiting our quantitative understanding of long-term changes of built-up areas. This is because commonly used large-scale mapping methods (e.g., computer vision) and suitable data sources (i.e., aerial imagery, remote sensing data, LiDAR data) have only been available in recent decades. However, there are alternative data sources such as cadastral records that are digitally available, containing relevant information such as building construction dates, allowing for an approximate, digital reconstruction of past building distributions. We conducted a non-exhaustive search of open and publicly available data resources from administrative institutions in the United States and gathered, integrated, and harmonized cadastral parcel data, tax assessment data, and building footprint data for 33 counties, wherever building footprint geometries and building construction year information was available. The result of this effort is a unique dataset that we call the Multi-Temporal Building Footprint Dataset for 33 U.S. Counties (MTBF-33). MTBF-33 contains over 6.2 million building footprints including their construction year, and can be used to derive retrospective depictions of built-up areas from 1900 to 2015, at fine spatial and temporal grain. Moreover, MTBF-33 can be employed for data validation purposes, or to train statistical learning models aiming to extract historical information on human settlements from remote sensing data, historical maps, or similar data sources.

Keywords: Building stock; Built environment; Change detection; Historical GIS; Human settlements; Long-term land use change; Remote sensing; Urbanization; historical spatial data.