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- Dokumenttyp:
- Forschungsdaten
- Veröffentlichungsdatum:
- 07.11.2022
- Verantwortlich:
- Zhu, Xiao Xiang
- Autorinnen / Autoren:
- Doda, Sugandha; Wang, Yuanyuan; Kahl, Matthias; Eike Jens Hoffmann; Kim Ouan, Taubenböck, Hannes; Zhu, Xiao Xiang
- Institutionszugehörigkeit:
- Doda, Sugandha (TUM); Wang, Yuanyuan (TUM); Kahl, Matthias (TUM); Eike Jens Hoffmann (TUM); Kim Ouan (TUM); Taubenböck, Hannes (DLR, JMU); Zhu, Xiao Xiang (TUM)
- Herausgeber:
- TUM
- Titel:
- So2Sat POP Part 1
- Identifikator:
- doi:10.14459/2021mp1633792
- Enddatum der Datenerzeugung:
- 01.10.2021
- Fachgebiet:
- DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
- zusätzliche Fachgebiete:
- Remote Sensing; Machine Learning; Population Modelling
- Quellen der Daten:
- Experimente und Beobachtungen / experiments and observations; Statistik und Referenzdaten / statistics and reference data
- Datentyp:
- Bilder / images; Texte / texts
- Methode der Datenerhebung:
- Semi-automatic data processing, Python and GDAL
- Beschreibung:
- 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 the digital elevation model.
- Links:
Link to the So2Sat POP Part 2: https://doi.org/10.14459/2021mp1633795
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
- Schlagworte:
- Remote sensing; deep learning; population estimation; population modelling; data fusion; gridded population distribution mapping; large‐scale population mapping; dasymetric modelling; sustainable development
- Technische Hinweise:
- View and download (39 GB total, 2 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1633792):
rsync rsync://m1633792@dataserv.ub.tum.de/m1633792/ - Sprache:
- en
- Rechte:
- by, http://creativecommons.org/licenses/by/4.0
- Horizon 2020:
- ERC-2016-StG-714087
BibTeX