IEEE Geoscience and Remote Sensing Magazine - December 2021 - 276

Contest Data
The data of the 2021 Data Fusion Contest and its Codalab evaluation websites
[Track DSE (https://competitions.codalab.org/competitions/27943) and Track
MSD (https://competitions.codalab.org/competitions/27956)] with the public
leaderboard will remain available for benchmarking algorithms and publishing
research works. The data are usable free of charge for scientific purposes, but
the contest terms and conditions on the contest webpage remain applicable
(http://www.classic.grss-ieee.org/community/technical-committees/data
-fusion/2021-ieee-grss-data-fusion-contest/). Please read them carefully.
previous editions is that the contest was not only a competition
but also led to subsequent collaborative projects and
real-world applications.
ACKNOWLEDGMENTS
The Image Analysis and Data Fusion Technical Committee
chairs would like to thank the IEEE GRSS for continuously
supporting the annual Data Fusion Content through funding
and resources.
Join the GRSS IADF TC
You can contact the Image Analysis Data Fusion Technical Committee (IADF
TC) chairs at iadf_chairs@grss-ieee.org. If you are interested in joining the
IADF TC, please complete the form on our website (http://www.grss-ieee.org/
community/technical-committees/data-fusion/) or send an email to us including
your
◗ first and last name
◗ institution/company
◗ country
◗ IEEE membership number (if available)
◗ email address.
Members receive information regarding research and applications on image
analysis and data fusion topics, and updates on the annual Data Fusion Contest
and on all other IADF TC activities. Membership in the IADF TC is free! You may
join the LinkedIn IEEE GRSS data fusion discussion forum, http://www.linkedin
.com/groups/IEEE-GRSS-Data-Fusion-Discussion-3678437, or join us on Twitter:
Grssiadf.
AUTHOR INFORMATION
Naoto Yokoya (yokoya@k.u-tokyo.ac.jp) is a lecturer at the
University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
Pedram Ghamisi (p.ghamisi@gmail.com) is the head of
the machine learning group, Helmholtz-Zentrum DresdenRossendorf,
Freiberg, D-09599, Germany.
Ronny Hänsch (rww.haensch@gmail.com) is with the
German Aerospace Center, Weßling, 82234, Germany.
Colin Prieur (colin.prieur@grenoble-inp.org) is with SICOM,
Grenoble INP, Grenoble, Rhône-Alpes, 38400, France.
Hana Malha (hana.malha@hpe.com) is with HPC&AI
Competency Center, Grenoble, 38320, France.
Jocelyn Chanussot (jocelyn.chanussot@grenoble-inp
.fr) is with Grenoble INP, Grenoble, 38400, France.
Caleb Robinson (caleb.robinson@microsoft.com) is
with Microsoft AI for Good Research Redmond, Washington,
98052, USA.
Kolya Malkin (kolya.malkin@yale.edu) is with Yale UniAt
the end of the competition, all winning teams wrote
a four-page paper on their approach, which was peer-reviewed
by the DFC organizing committee. These papers
were included in the technical program of IGARSS 2021
and were presented in an invited session on the DFC during
the symposium. All of these teams were awarded an
IEEE Certificate of Recognition for their winning participation
during the virtual award ceremony of IGARSS 2021
(see Figure 1). The first-, second-, and third-ranked teams
in each track received special prizes, thanks to the support
of the organizing partners. An extended article discussing
the winning solutions of the first- and second-ranked teams
will be submitted for peer review to the open access IEEE
Journal of Selected Topics in Applied Earth Observations and Remote
Sensing (JSTARS).
As in previous years, the 2021 DFC attracted participants
from a variety of disciplines, including AI and machine
learning as well as the remote sensing community.
The participation of such a diverse range of disciplines promotes
the development of novel and interdisciplinary approaches
to solve technical problems in the remote sensing
and geoscience communities, and also leads to a movement
to challenge global issues by bringing together knowledge
from different fields. The winning teams are all studentled,
and their extraordinary efforts have led to dramatic
advances in technology for the new problems addressed in
this competition and to the formation of a vibrant community.
One unique feature that differentiates the contest from
276
versity, New Haven, Connecticut, 06520, USA.
Nebojsa Jojic (jojic@microsoft.com) is with Microsoft
Research Redmond, Washington, 98052, USA.
REFERENCES
[1] N. Yokoya et al., " Open data for global multimodal land
use classification: Outcome of the 2017 IEEE GRSS data
fusion contest, " IEEE J. Sel. Topics Appl. Earth Observ. Remote
Sens., vol. 11, no. 5, pp. 1363-1377, May 2018, doi:
10.1109/JSTARS.2018.2799698.
[2] Y. Xu et al., " Advanced multi-sensor optical remote sensing
for urban land use and land cover classification: Outcome
of the 2018 IEEE GRSS data fusion contest, " IEEE J.
Sel. Topics Appl. Earth Observ. Remote Sens., vol. 12, no. 6, pp.
1709-1724, Jun. 2019, doi: 10.1109/JSTARS.2019.2911113.
[3] B. Le Saux, N. Yokoya, R. Hansch, M. Brown, and G. Hager,
" 2019 data fusion contest [Technical Committees], " IEEE
Geosci. Remote Sens. Mag., vol. 7, no. 1, pp. 103-105, Mar.
2019, doi: 10.1109/MGRS.2019.2893783.
[4] C. Robinson et al., " Global land-cover mapping with
weak supervision: Outcome of the 2020 IEEE GRSS data
fusion contest, " IEEE J. Sel. Topics Appl. Earth Observ. Remote
Sens., vol. 14, pp. 3185-3199, Mar. 2021, doi: 10.1109/
JSTARS.2021.3063849.
[5] N. Yokoya et al., " 2021 data fusion contest: Geospatial artificial
intelligence for social good [Technical Committees], "
IEEE Geosci. Remote Sens. Mag., vol. 9, no. 1, pp. 287-C3,
2021, doi: 10.1109/MGRS.2021.3055633.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE DECEMBER 2021

https://competitions.codalab.org/competitions/27943 https://competitions.codalab.org/competitions/27956 http://www.classic.grss-ieee.org/community/technical-committees/data-fusion/2021-ieee-grss-data-fusion-contest/ http://www.classic.grss-ieee.org/community/technical-committees/data-fusion/2021-ieee-grss-data-fusion-contest/ http://www.grss-ieee.org/community/technical-committees/data-fusion/ http://www.grss-ieee.org/community/technical-committees/data-fusion/ http://www.linkedin.com/groups/IEEE-GRSS-Data-Fusion-Discussion-3678437 http://www.linkedin.com/groups/IEEE-GRSS-Data-Fusion-Discussion-3678437

IEEE Geoscience and Remote Sensing Magazine - December 2021

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2021