Login
- Titel:
So2Sat LCZ42 v4: data with geolocation
- Dokumenttyp:
- Forschungsdaten
- Veröffentlichungsdatum:
- 31.03.2026
- Verantwortlich:
- Zhu, Xiao Xiang
- 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
- Identifikator:
- doi:10.14459/2025mp1836598.002
- Konzept-DOI:
- doi:10.14459/2025mp1836598
- Enddatum der Datenerzeugung:
- 23.03.2026
- 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 4th version with geolocation of all the training, validation, and testing patches, as well as the corresponding LCZ labels.
- Links:
This is version 4.2 (patches and labels with geolocation) of the dataset. See links of the other versions below.
Link to version 1: 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
This dataset relates to the publication: https://ieeexplore.ieee.org/document/9014553
- Schlagworte:
- Remote Sensing; Deep Learning; Change Detection; Semantic Segmentation
- Technische Hinweise:
- 4.2 View and download (56 GB total, 10 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1836598.002):
rsync rsync://m1836598.002@dataserv.ub.tum.de/m1836598.002/ - Sprache:
- en
- Rechte:
- by-sa, http://creativecommons.org/licenses/by-sa/4.0
- Horizon 2020:
- ERC-2016-StG-714087
BibTeX
Versionen
