IEEE Geoscience and Remote Sensing Magazine - December 2022 - 240

Remote Sensing Event and 2019 Joint Urban Remote Sensing
Event.
Xiao Xiang Zhu (xiaoxiang.zhu@tum.de) received her
M.Sc. degree, her Dr.-Ing. degree, and her Habilitation in
the field of signal processing from the Technical University
of Munich (TUM), Munich, Germany, in 2008, 2011,
and 2013, respectively. She is the chair professor for Data
Science In Earth Observation at TUM, Munich 80333, Germany,
and the head of the " EO Data Science " Department
at the Remote Sensing Technology Institute, German Aerospace
Center (DLR), Wessling 82234, Germany. Since 2019,
she has been a co-coordinator of the Munich Data Science
Research School (www.mu-ds.de). Since 2019, she has also
headed the Helmholtz Artificial Intelligence - Research
Field " Aeronautics, Space and Transport. " Since May 2020,
she has been the principal investigator and director of the
international future artificial intelligence (AI) lab " AI4EO
-Artificial Intelligence for Earth Observation: Reasoning,
Uncertainties, Ethics and Beyond, " Ottobrunn 85521, Germany.
Since October 2020, she has also served as a codirector
of the Munich Data Science Institute, TUM, Munich,
80333 Germany. She was a guest scientist or visiting
professor at the Italian National Research Council (CNRIREA),
Naples, Italy; Fudan University, Shanghai, China;
the University of Tokyo, Tokyo, Japan; and the University
of California, Los Angeles, USA in 2009, 2014, 2015, and
2016, respectively. She is currently a visiting AI professor
at the European Space Agency's Phi-lab. Her main
research interests are remote sensing and Earth observation;
signal processing; and machine learning and data
science, with their applications in tackling societal grand
challenges (e.g., global urbanization, the United Nations'
Sustainable Development Goals, and climate change).
She is a member of the Young Academy (Junge Akademie/
Junges Kolleg) at the Berlin-Brandenburg Academy of Sciences
and Humanities; the German National Academy of
Sciences Leopoldina; and the Bavarian Academy of Sciences
and Humanities. She serves on the scientific advisory
board in several research organizations, including
the German Research Center for Geosciences (GFZ) and
Potsdam Institute for Climate Impact Research (PIK). She
is an associate editor of IEEE Transactions on Geoscience and
Remote Sensing and serves as the area editor responsible
for special issues of IEEE Signal Processing Magazine. She is
a Fellow of IEEE.
REFERENCES
[1] Y. LeCun, Y. Bengio, and G. Hinton, " Deep learning, " Nature,
vol. 521, no. 7553, pp. 436-444, 2015, doi: 10.1038/nature14539.
[2]
J. Deng et al., " Imagenet: A large-scale hierarchical image database, "
in Proc. 2009 IEEE Conf. Comput. Vis. Pattern Recognit., pp.
248-255, doi: 10.1109/CVPR.2009.5206848.
[3] H. U. Dike et al., " Unsupervised learning based on artificial
neural network: A review, " in Proc. 2018 IEEE Int. Conf. Cyborg
Bionic Syst. (CBS), pp. 322-327, doi: 10.1109/CBS.2018.8612259.
240
[4] J. E. Van Engelen and H. H. Hoos, " A survey on semi-supervised
learning, " Mach. Learn., vol. 109, no. 2, pp. 373-440, 2020, doi:
10.1007/s10994-019-05855-6.
[5] Z.-H. Zhou, " A brief introduction to weakly supervised learning, "
Nat. Sci. Rev., vol. 5, no. 1, pp. 44-53, 2018, doi: 10.1093/
nsr/nwx106.
[6] J. Vanschoren, " Meta-learning, " in Automated Machine Learning.
Cham: Springer-Verlag, 2019, pp. 35-61.
[7] P. Goyal et al., " Self-supervised pretraining of visual features in
the wild, " 2021, arXiv:2103.01988.
[8] X. X. Zhu et al., " Deep learning in remote sensing: A comprehensive
review and list of resources, " IEEE Geosci. Remote
Sens. Mag. (replaces Newslett.), vol. 5, no. 4, pp. 8-36, 2017, doi:
10.1109/MGRS.2017.2762307.
[9] L. Jing and Y. Tian, " Self-supervised visual feature learning
with deep neural networks: A survey, " IEEE Trans. Pattern Anal.
Mach. Intell., vol. 43, no. 11, pp. 4037-4058, 2021, doi: 10.1109/
TPAMI.2020.2992393.
[10] A. Jaiswal et al., " A survey on contrastive self-supervised learning, "
Technologies, vol. 9, no. 1, p. 2, 2021, doi: 10.3390/technologies9010002.
[11]
X. Liu et al., " Self-supervised learning: Generative or contrastive, "
IEEE Trans. Knowl. Data Eng., early access, 2021, doi:
10.1109/TKDE.2021.3090866.
[12] K. Ohri and M. Kumar, " Review on self-supervised image
recognition using deep neural networks, " Knowl.-Based Syst.,
vol. 224, p. 107,090, Jul. 2021, doi: 10.1016/j.knosys.2021.
107090.
[13] G. Sumbul et al., " BigEarthNet: A large-scale benchmark archive
for remote sensing image understanding, " in Proc. IGARSS
2019 IEEE Int. Geosci. Remote Sens. Symp., pp. 5901-5904, doi:
10.1109/IGARSS.2019.8900532.
[14] M. Schmitt et al., " SEN12MS - A curated dataset of georeferenced
multi-spectral sentinel-1/2 imagery for deep learning
and data fusion, " 2019, arXiv:1906.07789.
[15] X. X. Zhu et al., " So2Sat LCZ42: A benchmark dataset for global
local climate zones classification, " 2019, arXiv:1912.12171.
[16] F. Zhuang et al., " A comprehensive survey on transfer learning, "
Proc. IEEE, vol. 109, no. 1, pp. 43-76, 2020, doi: 10.1109/
JPROC.2020.3004555.
[17] K. He et al., " Deep residual learning for image recognition, " in
Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 770-
778, doi: 10.1109/CVPR.2016.90.
[18] A. Dosovitskiy et al., " An image is worth 16x16 words: Transformers
for image recognition at scale, " 2020, arXiv:2010.11929.
[19] I. Dópido et al., " Semisupervised self-learning for hyperspectral
image classification, " IEEE Trans. Geosci. Remote Sens., vol. 51,
no. 7, pp. 4032-4044, 2013, doi: 10.1109/TGRS.2012.2228275.
[20] F. Ratle, G. Camps-Valls, and J. Weston, " Semisupervised neural
networks for efficient hyperspectral image classification, "
IEEE Trans. Geosci. Remote Sens., vol. 48, no. 5, pp. 2271-2282,
2010, doi: 10.1109/TGRS.2009.2037898.
[21] Y. Wang et al., " Self-supervised low-rank representation
(SSLRR) for hyperspectral image classification, " IEEE Trans.
Geosci. Remote Sens., vol. 56, no. 10, pp. 5658-5672, 2018, doi:
10.1109/TGRS.2018.2823750.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE DECEMBER 2022
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IEEE Geoscience and Remote Sensing Magazine - December 2022

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