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

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 becaus...

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Detalles Bibliográficos
Autores principales: Uhl, Johannes H., Leyk, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233215/
https://www.ncbi.nlm.nih.gov/pubmed/35761991
http://dx.doi.org/10.1016/j.dib.2022.108369
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author Uhl, Johannes H.
Leyk, Stefan
author_facet Uhl, Johannes H.
Leyk, Stefan
author_sort Uhl, Johannes H.
collection PubMed
description 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.
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spelling pubmed-92332152022-06-26 MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 – 2015) Uhl, Johannes H. Leyk, Stefan Data Brief Data Article 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. Elsevier 2022-06-13 /pmc/articles/PMC9233215/ /pubmed/35761991 http://dx.doi.org/10.1016/j.dib.2022.108369 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Uhl, Johannes H.
Leyk, Stefan
MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 – 2015)
title MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 – 2015)
title_full MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 – 2015)
title_fullStr MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 – 2015)
title_full_unstemmed MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 – 2015)
title_short MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 – 2015)
title_sort mtbf-33: a multi-temporal building footprint dataset for 33 counties in the united states (1900 – 2015)
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9233215/
https://www.ncbi.nlm.nih.gov/pubmed/35761991
http://dx.doi.org/10.1016/j.dib.2022.108369
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