Doda, Sugandha und Wang, Yuanyuan und Kahl, Matthias und Hoffmann, Eike Jens und Ouan, Kim und Taubenböck, Hannes und Zhu, Xiao Xiang (2022) So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale. Scientific Data, 9, 715_1-715_11. Nature Publishing Group. doi: 10.1038/s41597-022-01780-x. ISSN 2052-4463.
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Offizielle URL: https://www.nature.com/articles/s41597-022-01780-x
Kurzfassung
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.
elib-URL des Eintrags: | https://elib.dlr.de/192673/ | ||||||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||||||
Titel: | So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale | ||||||||||||||||||||||||||||||||
Autoren: |
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Datum: | November 2022 | ||||||||||||||||||||||||||||||||
Erschienen in: | Scientific Data | ||||||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||||||
Band: | 9 | ||||||||||||||||||||||||||||||||
DOI: | 10.1038/s41597-022-01780-x | ||||||||||||||||||||||||||||||||
Seitenbereich: | 715_1-715_11 | ||||||||||||||||||||||||||||||||
Verlag: | Nature Publishing Group | ||||||||||||||||||||||||||||||||
ISSN: | 2052-4463 | ||||||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||||||
Stichwörter: | data sets, population estimation, digital elevation model, local climate zone, land use proportions, nighttime lights | ||||||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit | ||||||||||||||||||||||||||||||||
Hinterlegt von: | Haschberger, Dr.-Ing. Peter | ||||||||||||||||||||||||||||||||
Hinterlegt am: | 20 Dez 2022 09:56 | ||||||||||||||||||||||||||||||||
Letzte Änderung: | 29 Mär 2023 00:03 |
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