So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]

Xiao Xiang Zhu; Jingliang Hu; Chunping Qiu; Yilei Shi; Jian Kang; Lichao Mou; Hossein Bagheri; Matthias Haberle; Yuansheng Hua; Rong Huang; Lloyd Hughes; Hao Li; Yao Sun; Guichen Zhang; Shiyao Han; Michael Schmitt; Yuanyuan Wang

doi:10.1109/MGRS.2020.2964708

So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]

Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu, Yilei Shi, Jian Kang, Lichao Mou, Hossein Bagheri, Matthias Haberle, Yuansheng Hua, Rong Huang, Lloyd Hughes, Hao Li, Yao Sun, Guichen Zhang, Shiyao Han, Michael Schmitt, Yuanyuan Wang

Chair of Data Science in Earth Observation

Research output: Contribution to journal › Article › peer-review

109 Scopus citations

Abstract

Gaining access to labeled reference data is one of the great challenges in supervised machine-learning endeavors. This is especially true for an automated 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.

Original language	English
Article number	9014553
Pages (from-to)	76-89
Number of pages	14
Journal	IEEE Geoscience and Remote Sensing Magazine
Volume	8
Issue number	3
DOIs	https://doi.org/10.1109/MGRS.2020.2964708
State	Published - Sep 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/MGRS.2020.2964708

Cite this

Zhu, X. X., Hu, J., Qiu, C., Shi, Y., Kang, J., Mou, L., Bagheri, H., Haberle, M., Hua, Y., Huang, R., Hughes, L., Li, H., Sun, Y., Zhang, G., Han, S., Schmitt, M., & Wang, Y. (2020). So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]. IEEE Geoscience and Remote Sensing Magazine, 8(3), 76-89. Article 9014553. https://doi.org/10.1109/MGRS.2020.2964708

@article{dc8297a48af4459095e6eef652bcfaa8,

title = "So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]",

abstract = "Gaining access to labeled reference data is one of the great challenges in supervised machine-learning endeavors. This is especially true for an automated 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.",

author = "Zhu, {Xiao Xiang} and Jingliang Hu and Chunping Qiu and Yilei Shi and Jian Kang and Lichao Mou and Hossein Bagheri and Matthias Haberle and Yuansheng Hua and Rong Huang and Lloyd Hughes and Hao Li and Yao Sun and Guichen Zhang and Shiyao Han and Michael Schmitt and Yuanyuan Wang",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2020",

month = sep,

doi = "10.1109/MGRS.2020.2964708",

language = "English",

volume = "8",

pages = "76--89",

journal = "IEEE Geoscience and Remote Sensing Magazine",

issn = "2473-2397",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "3",

}

Zhu, XX, Hu, J, Qiu, C, Shi, Y, Kang, J, Mou, L, Bagheri, H, Haberle, M, Hua, Y, Huang, R, Hughes, L, Li, H, Sun, Y, Zhang, G, Han, S, Schmitt, M & Wang, Y 2020, 'So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]', IEEE Geoscience and Remote Sensing Magazine, vol. 8, no. 3, 9014553, pp. 76-89. https://doi.org/10.1109/MGRS.2020.2964708

TY - JOUR

T1 - So2Sat LCZ42

T2 - A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]

AU - Zhu, Xiao Xiang

AU - Hu, Jingliang

AU - Qiu, Chunping

AU - Shi, Yilei

AU - Kang, Jian

AU - Mou, Lichao

AU - Bagheri, Hossein

AU - Haberle, Matthias

AU - Hua, Yuansheng

AU - Huang, Rong

AU - Hughes, Lloyd

AU - Li, Hao

AU - Sun, Yao

AU - Zhang, Guichen

AU - Han, Shiyao

AU - Schmitt, Michael

AU - Wang, Yuanyuan

PY - 2020/9

Y1 - 2020/9

N2 - Gaining access to labeled reference data is one of the great challenges in supervised machine-learning endeavors. This is especially true for an automated 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.

AB - Gaining access to labeled reference data is one of the great challenges in supervised machine-learning endeavors. This is especially true for an automated 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.

UR - http://www.scopus.com/inward/record.url?scp=85080145487&partnerID=8YFLogxK

U2 - 10.1109/MGRS.2020.2964708

DO - 10.1109/MGRS.2020.2964708

M3 - Article

AN - SCOPUS:85080145487

SN - 2473-2397

VL - 8

SP - 76

EP - 89

JO - IEEE Geoscience and Remote Sensing Magazine

JF - IEEE Geoscience and Remote Sensing Magazine

IS - 3

M1 - 9014553

ER -

So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]

Abstract

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this