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- Document type:
- Forschungsdaten
- Publication date:
- 28.09.2018
- Authors:
- 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
- Author affiliation:
- Zhu, Xiaoxiang (TUM, DLR); Hu, Jingliang (DLR); Qiu, Chunping (TUM); Shi, Yilei (TUM); Bagheri, Hossein (TUM); Kang, Jian (TUM); Li, Hao (TUM); Mou, Lichao (TUM); Zhang, Guicheng (TUM); Häberle, Matthias (DLR); Han, Shiyao (TUM); Hua, Yuansheng (TUM); Huang, Rong (TUM); Hughes, Lloyd (TUM); Sun, Yao (DLR); Schmitt, Michael (TUM); Wang, Yuanyuan (TUM)
- Publisher:
- TUM
- Title:
- So2Sat LCZ42
- Identifier:
- doi:10.14459/2018MP1454690
- End date of data production:
- 30.08.2018
- Subject area:
- BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
- Resource type:
- Abbildungen von Objekten / image of objects
- Data type:
- Bilder / images
- Description:
- 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.
- Method of data assessment:
- 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.
- Links:
This is version 1 of the dataset. See download link below
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- Key words:
- local climate zones ; big data ; classification ; remote sensing ; deep learning ; data fusion ; synthetic aperture radar imagery ; optical imagery
- Technical remarks:
- View and download (51.8 GB, 6 files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1454690):
rsync rsync://m1454690@dataserv.ub.tum.de/m1454690/ - Language:
- en
- Rights:
- by, http://creativecommons.org/licenses/by/4.0
- Horizon 2020:
- ERC-2016-StG-714087
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