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

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, usin...

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Veröffentlicht in:	IEEE geoscience and remote sensing magazine 2020-09, Vol.8 (3), p.76-89
Hauptverfasser:	Zhu, Xiao Xiang, Hu, Jingliang, Qiu, Chunping, Shi, Yilei, Kang, Jian, Mou, Lichao, Bagheri, Hossein, Haberle, Matthias, Hua, Yuansheng, Huang, Rong, Hughes, Lloyd, Li, Hao, Sun, Yao, Zhang, Guichen, Han, Shiyao, Schmitt, Michael, Wang, Yuanyuan
Format:	Artikel
Sprache:	eng
Schlagworte:	Climate change Earth Labeling Machine learning Remote sensing Urban areas
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