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Title:

So2Sat POP Part 2

Document type:
Forschungsdaten
Publication date:
07.11.2022
Responsible:
Zhu, Xiao Xiang
Authors:
Doda, Sugandha; Wang, Yuanyuan; Kahl, Matthias; Eike Jens Hoffmann; Kim Ouan, Taubenböck, Hannes; Zhu, Xiao Xiang
Author affiliation:
Doda, Sugandha (TUM); Wang, Yuanyuan (TUM); Kahl, Matthias (TUM); Eike Jens Hoffmann (TUM); Kim Ouan (TUM); Taubenböck, Hannes (DLR, JMU); Zhu, Xiao Xiang (TUM)
Publisher:
TUM
Identifier:
doi:10.14459/2021mp1633795
End date of data production:
01.10.2021
Subject area:
DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
Other subject areas:
Remote Sensing; Machine Learning; Population Modelling
Resource type:
Experimente und Beobachtungen / experiments and observations; Statistik und Referenzdaten / statistics and reference data
Data type:
Bilder / images; Texte / texts
Description:
A comprehensive data set for population estimation in 106 European cities. The final data set consists of two parts: So2Sat POP Part 1 and So2Sat POP Part 2. So2Sat POP Part 1 comprises local climate zone, land use classifications, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. So2Sat POP Part 2 consists of digital elevation model.
Method of data assessment:
Semi-automatic data processing, Python and GDAL
Links:

 

Link to the So2Sat POP Part1: https://doi.org/10.14459/2021mp1633792

Link to the Source Code: https://github.com/zhu-xlab/So2Sat-POP

Link to the corresponding article https://doi.org/10.1038/s41597-022-01780-x

Key words:
Remote sensing; deep learning; population estimation; population modelling; data fusion; gridded population distribution mapping; large‐scale population mapping; dasymetric modelling; sustainable development
Technical remarks:
View and download (824 MB total, 2 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1633795):
rsync rsync://m1633795@dataserv.ub.tum.de/m1633795/
Language:
en
Rights:
by-sa, http://creativecommons.org/licenses/by-sa/4.0
Horizon 2020:
ERC-2016-StG-714087
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