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So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale
Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The tradi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675793/ https://www.ncbi.nlm.nih.gov/pubmed/36402846 http://dx.doi.org/10.1038/s41597-022-01780-x |
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author | Doda, Sugandha Wang, Yuanyuan Kahl, Matthias Hoffmann, Eike Jens Ouan, Kim Taubenböck, Hannes Zhu, Xiao Xiang |
author_facet | Doda, Sugandha Wang, Yuanyuan Kahl, Matthias Hoffmann, Eike Jens Ouan, Kim Taubenböck, Hannes Zhu, Xiao Xiang |
author_sort | Doda, Sugandha |
collection | PubMed |
description | Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, statistical and machine learning methods have been developed to estimate population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated approaches in the field of population estimation. |
format | Online Article Text |
id | pubmed-9675793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96757932022-11-21 So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale Doda, Sugandha Wang, Yuanyuan Kahl, Matthias Hoffmann, Eike Jens Ouan, Kim Taubenböck, Hannes Zhu, Xiao Xiang Sci Data Data Descriptor Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, statistical and machine learning methods have been developed to estimate population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated approaches in the field of population estimation. Nature Publishing Group UK 2022-11-19 /pmc/articles/PMC9675793/ /pubmed/36402846 http://dx.doi.org/10.1038/s41597-022-01780-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Doda, Sugandha Wang, Yuanyuan Kahl, Matthias Hoffmann, Eike Jens Ouan, Kim Taubenböck, Hannes Zhu, Xiao Xiang So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale |
title | So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale |
title_full | So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale |
title_fullStr | So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale |
title_full_unstemmed | So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale |
title_short | So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale |
title_sort | so2sat pop - a curated benchmark data set for population estimation from space on a continental scale |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675793/ https://www.ncbi.nlm.nih.gov/pubmed/36402846 http://dx.doi.org/10.1038/s41597-022-01780-x |
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