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 Häberle, Yuansheng Hua, Rong Huang, Lloyd Haydn Hughes, Hao Li, Yao Sun, Guichen Zhang, Shiyao Han, Michael Schmitt, Yuanyuan Wang
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 <i>Sentinel-1</i> and <i>Sentinel-2</i> image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe.