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gremlin97
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RemoteSensingCorpus

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deep learning will play on causal discovery is, at best, uncer -
tain because deep learning models focus mostly on fitting
and are largely overparameterized, which is (apparently)
against causal, sparse, reasoning. Only very recently have
we witnessed efforts toward either incorporating or under -
standing deep models causally: the authors in [ 89] imple -
mented a metalearning objective that maximizes the speed of domain transfer, which, under certain assumptions, can
be seen as a way to localize changes in causal mechanisms.
In [90], the authors learned individual-level causal effects
from observational data that can efficiently handle con -
founding (hidden) factors. Both methods are, in principle,
well suited to the problems in remote sensing and geosci -
ence data sets, which exhibit spatiotemporal relationships
to be exploited but have not (thus far) been considered.
Yet we will have to face a more important challenge:
cognitive barriers. Domain knowledge is elusive and dif -
ficult to encode, interaction between computer scientists
and physicists is still a barrier, and education in synergistic
concepts still needs to become a reality in coming years.
Causal inference is believed to be the best approach to de -
velop Earth sciences, but this will be possible with a strong
and continuous interaction between domain knowledge
experts and computer scientists.
CONCLUSIONS
This article described the six ideas and six directions
in which geosciences, Earth observation, and AI can
achieve a lot if synergistically combined. With this ar -
ticle, we have provided our appreciation for research av -
enues that are new, refreshing, and exciting for scientists
willing to evolve at the interface between AI and the geo -
sciences. We hope that they will spark curiosity and that
the community, especially the younger generations, will
embrace them.
ACKNOWLEDGMENTS
Xiao Xiang Zhu is jointly supported by the European Re -
search Council (ERC) under grant ERC-2016-StG-714087,
by the Helmholtz Association through the Framework of
Helmholtz Artificial Intelligence Cooperation Unit and
Helmholtz Excellent Professorship Data Science in Earth
Observation—Big Data Fusion for Urban Research, and by
the German Federal Ministry of Education and Research
in the framework of the international future AI lab AI4EO.
Gustau Camps-Valls was partly funded by the ERC un -
der the ERC-SyG-2019 USMILE project (grant agreement
855187). Nathan Jacobs was partly funded by a National
Science Foundation CAREER Award (IIS-1553116). Devis
Tuia is the corresponding author.
Some of the ideas presented in this article originated
from discussions during the first workshop of the ELLIS
Program ML for Earth and Climate Science (Germany) a
few days before the COVID-19 lockdown in Europe.
AUTHOR INFORMATION
Devis Tuia (devis.tuia@epfl.ch) is with Ecole polytechnique
fédérale de Lausanne, Sion, 1950, Switzerland. He is the
corresponding author for this article. He is a Senior Mem -
ber of IEEE.
Ribana Roscher (ribana.roscher@uni-bonn.de) is with
the University of Bonn, Bonn, 53115, Germany. She is a
Member of IEEE.
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102
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2021Jan Dirk Wegner (jan.wegner@geod.baug.ethz.ch) is
with ETH Zürich, Zürich, 8093, Switzerland.
Nathan Jacobs (nathan.jacobs@uky.edu) is with the
University of Kentucky, Lexington, Kentucky, 40506-0633,
USA. He is a Senior Member of IEEE.
Xiao Xiang Zhu (xiaoxiang.zhu@dlr.de) is with the Techni -
cal University of Munich, Munich, 80333, Germany, and the
German Aerospace Center, Wessling, Bavaria, 82234, Ger -
many. She is a Senior Member of IEEE. He is a Fellow of IEEE.
Gustau Camps-Valls (gustau.camps@uv.es) is with the
Universitat de València, Paterna, València, 46980, Spain.
He is a Fellow of IEEE.
REFERENCES
[1] M. Reichstein , G. Camps-Valls , B. Stevens , M. Jung, and J. Den-
zler, “Deep learning and process understanding for data-driven
earth system science ,” Nature , vol. 566, no. 7743 , pp. 195–204,
2019 . doi: 10.1038/s41586-019-0912-1 .
[2] G. Camps-Valls , D. Tuia, X. X. Zhu, and M. Reichstein , Deep Learn -
ing for Earth Sciences: A Comprehensive Approach to Remote Sensing,
Climate Science and Geosciences . Oxford , U.K.: Wiley , 2021 .
[3] X. Zhu et al., “Deep learning in remote sensing: A comprehensive
review and list of resources ,” IEEE Geosci. Remote Sens. Mag. , vol.
5, no. 4, pp. 8–36, 2017. doi: 10.1109/MGRS.2017.2762307 .
[4] N. Audebert , B. Le Saux , and S. Lefevre , “Deep learning for clas -
sification of hyperspectral data: A comparative review ,” IEEE
Geosci. Remote Sens. Mag. , vol. 7, no. 2, pp. 159–173, 2019 . doi:
10.1109/MGRS.2019.2912563 .
[5] Q. Yuan et al., “Deep learning in environmental remote sensing:
Achievements and challenges ,” Remote Sens. Environ. , vol. 241, p.
111,716, May 2020 . doi: 10.1016/j.rse.2020.111716 .
[6] L. Mou, Y. Hua, and X. X. Zhu, “A relation-augmented fully con -
volutional network for semantic segmentation in aerial scenes ,”
in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019 , pp.
12,416 –12,425 . doi: 10.1109/CVPR.2019.01270 .
[7] L. Mou, Y. Hua, P. Jin, and X. X. Zhu, “ERA: A dataset and deep
learning benchmark for event recognition in aerial videos ,” IEEE
Geosci. Remote Sens. Mag. , vol. 8, no. 4, pp. 125–133, Dec. 2020,
doi: 10.1109/MGRS.2020.3005751.
[8] Y. Aytar , C. Vondrick , and A. Torralba , “See, hear, and read: Deep