Login
- Dokumenttyp:
- Forschungsdaten
- Veröffentlichungsdatum:
- 13.11.2023
- Verantwortlich:
- Zhu, Xiaoxiang
- Autorinnen / Autoren:
- 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
- Institutionszugehörigkeit:
- TUM
- Herausgeber:
- TUM
- Titel:
- So2Sat LCZ42
- Identifikator:
- doi:10.14459/2022mp1659039
- 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. In this version of the dataset, we included additionally the label confidence evaluation data. Ten European cities in the city list were re-labeled by having a group of remote sensing experts cast 10 independent votes on a subset of the originally labeled p... »
- Links:
This is version 4 of the dataset. See download link below
Link to version 1 (containing additional test data): https://mediatum.ub.tum.de/1454690
Link to version 2 (containing additional test data): https://mediatum.ub.tum.de/1483140
Link to version 3 (containing 2 more training testing splits): https://mediatum.ub.tum.de/1613658
A detailed description of this dataset, and its different versions: https://github.com/zhu-xlab/So2Sat-LCZ42
Corresponding article can be found under: https://doi.org/10.1109/TGRS.2023.3336357- Schlagworte:
- local climate zones ; big data ; classification ; remote sensing ; deep learning ; data fusion ; synthetic aperture radar imagery ; optical imagery
- Technische Hinweise:
- View and download (31 GB total, 5 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1659039):
rsync rsync://m1659039@dataserv.ub.tum.de/m1659039/ - Sprache:
- en
- Rechte:
- by, http://creativecommons.org/licenses/by/4.0
- Horizon 2020:
- ERC-2016-StG-714087
BibTeX