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Titel:

So2Sat LCZ42 3 splits

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
23.06.2021
Verantwortlich:
Zhu, Xiaoxiang ; Hu, Jingliang ; Qiu, Chunping ; Shi, Yilei ; Bagheri, Hossein ; Kang, Jian ; Li, Hao ; Mou, Lichao ; Zhang, Guicheng ; Häberle, Matthias ; Han, Shiyao ; Hua, Yuansheng ; Huang, Rong ; Hughes, Lloyd ; Sun, Yao ; Schmitt, Michael ; Wang, Yuanyuan
Autorinnen / Autoren:
Zhu, Xiaoxiang
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Enddatum der Datenerzeugung:
30.08.2018
Fachgebiet:
BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
Quellen der Daten:
Abbildungen von Objekten / image of objects
Datentyp:
Bilder / images
Methode der Datenerhebung:
Sentinel-1 image downloaded from ESA SciHub, and prepared by ESA SNAP software. Sentinel-2 image semi-automatically downloaded and prepared using Google Earth Engine and MATLAB. The local climate zones labels were manually labeled.
Beschreibung:
So2Sat LCZ42 is a dataset consisting of co-registered synthetic aperture radar and multispectral optical image patches acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and the corresponding local climate zones (LCZ) label. The dataset is distributed over 42 cities across different continents and cultural regions of the world. This is the "3 splits version" of the So2Sat LCZ42 dataset. It provides three training/testing data split scenarios: 1. Random split: 80% train...     »
Links:

Article: https://ieeexplore.ieee.org/document/9014553
DOI: 10.1109/MGRS.2020.2964708
README: https://github.com/zhu-xlab/So2Sat-LCZ42

Schlagworte:
local climate zones ; big data ; classification ; remote sensing ; deep learning ; data fusion ; synthetic aperture radar imagery ; optical imagery
Technische Hinweise:
View and download (165 GB total, 10 files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1613658):
rsync rsync://m1613658@dataserv.ub.tum.de/m1613658/
Sprache:
en
Rechte:
by, http://creativecommons.org/licenses/by/4.0
Horizon 2020:
ERC-2016-StG-714087
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