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Dokumenttyp:
Forschungsdaten 
Veröffentlichungsdatum:
02.12.2021 
Autorinnen / Autoren:
Zhu, Xiaoxiang ; Qiu, Chunping ; Hu, Jingliang ; Shi, Yilei; Wang, Yuanyuan ; Schmitt, Michael 
Institutionszugehörigkeit:
Zhu, Xiaoxiang (TUM, DLR) ; Qiu, Chunping (TUM) ; Hu, Jingliang (TUM) ; Shi, Yilei (TUM) ; Wang, Yuanyuan (TUM, DLR); Schmitt, Michael (TUM) ; 
Herausgeber:
TUM 
Titel:
So2Sat GUL - So2Sat Global Urban LCZs 
Identifikator:
Zeit der Erzeugung:
06.10.2020 
Fachgebiet:
DAT Datenverarbeitung, Informatik; GEO Geowissenschaften; UMW Umweltwissenschaften 
zusätzliche Fachgebiete:
Remote Sensing, Earth observation, machine learning 
Quellen der Daten:
Experimente und Beobachtungen / experiments and observations; Abbildungen von Objekten / image of objects 
Datentyp:
Bilder / images 
Methode der Datenerhebung:
The local climate zones classification takes input from one Sentinel-1 image and four seasonal Sentinel-2 images as inputs. The Sentinel-1 images were downloaded from ESA SciHub, and prepared using ESA SNAP software. Sentinel-2 images were semi-automatically downloaded and prepared using Google Earth Engine and MATLAB. The local climate zones classification labels were predicted using convolutional neural network with a model pre-trained on the "So2Sat LCZ42" training data (https://doi.org/10.14...    »
 
Beschreibung:
So2Sat GUL consists of the local climate zones classification maps of 1692 cities whose population are larger than 300,000 according to the United Nations' World Urbanization Prospects: The 2014 Revision. 
Links:
This dataset relates to the publication: 10.1016/j.rse.2021.112794  
Schlagworte:
Local climate zones, Sentinel-1, Sentinel-2, machine learning, convolutional neural network, So2Sat 
Technische Hinweise:
View and download (6,63 GB total, 13535 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1633461):
rsync rsync://m1633461@dataserv.ub.tum.de/m1633461/ 
Sprache:
en 
Rechte:
by, http://creativecommons.org/licenses/by/4.0 
Horizon 2020:
ERC-2016-StG-714087