Prof. Dr. Xiaoxiang Zhu

Academic Career and Research Areas

The research of Professor Zhu (b. 1984) focuses on signal processing and data science in earth observation. Geoinformation derived from Earth observation satellite data is indispensable for many scientific, governmental and planning tasks. Furthermore, Earth observation has arrived in the Big Data era with ESA's Sentinel satellites and NewSpace companies. Professor Zhu develops explorative signal processing and machine learning algorithms, such as compressive sensing and deep learning, to improve information retrieval from remote sensing data, and to enable breakthroughs in geoscientific and environmental research. In particular, by the fusion of petabytes of EO data from satellite to social media, she aims at tackling challenges such as mapping of global urbanization.

Professor Zhu studied aerospace engineering in China and at TUM, where she also received her doctorate (2011) and postdoctoral teaching qualification (habilitation) in 2013. She leads a Helmholtz junior university research group at the German Aerospace Center (DLR) and TUM since 2013, and held visiting scholar positions in Italy, China, Japan and the US. In 2015, Professor Zhu was appointed as a professor at TUM and has since 2018 also headed the EO Data Science department at DLR.

Awards

  • Heinz Maier-Leibnitz Medal, TUM (2018)
  • Leopoldina Early Career Award (2018)
  • PRACE Ada Lovelace Award for HPC (2018)
  • Helmholtz Excellence Professorship (2017)
  • ERC Starting Grant (2016)

Zhu X, Qiu C, Hu J, Shi Y, Wang Y, Schmitt M, Taubenböck H: "The Urban Morphology on Our Planet - Global perspectives from Space". Remote Sensing of Environment. 2022; 269: 112794.

Abstract

Zhu X, Montazeri S, Ali M, Hua Y, Wang Y, Mou L, Shi Y, Xu F, Bamler R: "Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives". IEEE Geoscience and Remote Sensing Magazine. 2021; 9(4):143-172.

Abstract

Camps-Valls G, Tuia D, Zhu X, Reichstein M: Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences. Wiley, 2021.

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Zhu X, Tuia D, Mou L, Xia G, Zhang L, Xu F, Fraundorfer F: "Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources". IEEE Geoscience and Remote Sensing Magazine. 2017; 5(4):8-36.

Abstract

Zhu X, Bamler R: "Tomographic SAR Inversion by L1 Norm Regularization – The Compressive Sensing Approach". IEEE Transactions on Geoscience and Remote Sensing. 2010; 48(10): 3839-3846. 

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