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So2Sat LCZ42: A benchmark data set for the classification of global local climate zones

Zhu, Xiao Xiang and Hu, Jingliang and Qiu, Chunping and Shi, Yilei and Kang, Jian and Mou, LiChao and Bagheri, Hossein and Häberle, Matthias and Hua, Yuansheng and Huang, Rong and Hughes, Lloyd and Li, Hao and Sun, Yao and Zhang, Guichen and Han, Shiyao and Schmitt, Michael and Wang, Yuanyuan (2020) So2Sat LCZ42: A benchmark data set for the classification of global local climate zones. IEEE Geoscience and Remote Sensing Magazine (GRSM), 8 (3), pp. 76-89. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/MGRS.2020.2964708. ISSN 2168-6831.

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Official URL: https://ieeexplore.ieee.org/document/9014553

Abstract

Gaining access to labeled reference data is one of the great challenges in supervised machine-learning endeavors. This is especially true for an automat ed analysis of remote sensing images on a global scale, which enables us to address global challenges, such as urbanization and climate change, using state-of-the-art machine-learning techniques. To meet these pressing needs, especially in urban research, we provide open access to a valuable benchmark data set, So2Sat LCZ42, which consists of local-climate-zone (LCZ) labels of approximately half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe.

Item URL in elib:https://elib.dlr.de/138056/
Document Type:Article
Title:So2Sat LCZ42: A benchmark data set for the classification of global local climate zones
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Zhu, Xiao XiangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hu, JingliangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Qiu, ChunpingTechnichal University MünchenUNSPECIFIEDUNSPECIFIED
Shi, YileiTU-MünchenUNSPECIFIEDUNSPECIFIED
Kang, JianTUMUNSPECIFIEDUNSPECIFIED
Mou, LiChaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bagheri, HosseinTU MünchenUNSPECIFIEDUNSPECIFIED
Häberle, MatthiasUNSPECIFIEDhttps://orcid.org/0000-0001-9550-5252UNSPECIFIED
Hua, YuanshengUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Huang, RongUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hughes, LloydUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Li, HaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Sun, YaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhang, GuichenUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Han, ShiyaoTUM-SiPEOUNSPECIFIEDUNSPECIFIED
Schmitt, MichaelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wang, YuanyuanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:26 February 2020
Journal or Publication Title:IEEE Geoscience and Remote Sensing Magazine (GRSM)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:8
DOI:10.1109/MGRS.2020.2964708
Page Range:pp. 76-89
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:2168-6831
Status:Published
Keywords:Labeling, Urban areas, Remote sensing, Machine learning, Earth, Google, Buildings
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hu, Jingliang
Deposited On:27 Nov 2020 16:10
Last Modified:24 Oct 2023 12:50

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