Complex Data Challenges in Earth Observation Call for Papers

CDCEO 2022
2nd workshop on Complex Data Challenges in Earth Observation
Vienna, Austria, July 23-25th, 2022 (exact date TBD)
Co-located with IJCAI-ECAI 2022

Submission deadline: May 31st, 2022
Workshop website:


The Big Data accumulating from remote sensing technology in ground,
aerial, and satellite-based Earth Observation (EO) has radically
changed how we monitor the state of our planet. The ever-growing
availability of high-resolution remote sensing data increasingly
confronts researchers with the unique machine learning challenges
posed by characteristic heterogeneity and correlation structures in
these data.

In this workshop we will bring together leading researchers from both
academia and industry across diverse domains of AI, including experts
from AI, big data, remote sensing, computer vision, spatio-temporal
data processing, geographic information systems, and weather and
climate modelling, as well as other scientists or engineers with a
general interest in the application of modern data analysis methods
within the EO domain.

This workshop is organised as a physical meeting and is part of
IJCAI-ECAI 2022, the 31st International Joint Conference on Artificial
Intelligence and the 25th European Conference on Artificial


The workshop invites advanced applications and method development in
image and signal processing, data fusion, feature extraction, meta
learning, and many more.

The topics covered by the workshop theme include but are not limited to:
Trustworthy AI for Earth observation
Physics-informed machine learning for Earth observation
Human-in-the-loop Earth observation data analysis
Edge AI for Earth observation
Vision and language for Earth observation
Fairness and accountability in Earth observation data analysis
Spatio-temporal data processing and analysis
Multi-resolution, multi-temporal, multi-sensor, and multi-modal Earth observation data fusion
Machine learning for weather and climate research
Deep learning and its applications to, e.g., semantic segmentation, scene classification, and feature extraction
Meta learning, including transfer learning, few-shot learning, and active learning
Integration and aggregation of complementary remote sensing measurements
Benchmark datasets with applications to Earth Observation


Submission starts: April 1st, 2022
Workshop paper submission deadline: May 31st, 2022
Notification of paper acceptance: June 15th, 2022
Camera-ready paper submission deadline: June 30th, 2022
Workshop date: July 23-25th, 2022 (exact date TBD)


Authors are invited to submit original papers presenting research,
position papers or papers presenting research in progress that have
not been previously published, and are not being considered for
publication elsewhere. Blind reviewing process performed by members of
the Program Committee will be applied to select papers based on their
novelty, technical quality, potential impact, clarity, and

Workshop papers will be included in a Workshop Proceedings published
by Papers must be formatted in CEUR two column
style guidelines. The page limit is 4  6 pages plus references.

At least one of the authors of the accepted papers must register for
the workshop for the paper to be included into the workshop

Please use the following link to submit your contribution:


A special session of the workshop will present the winning solutions
and highlights from a unique Landslide4Sense competition

Realistic data for training and testing machine learning models has
become vitally important for many branches of cutting-edge research in
EO. The aim of Landslide4Sense is to promote innovative algorithms for
automatic landslide detection using globally distributed remotely
sensed images, as well as to provide objective and fair comparisons
among different methods. discloses a unique large-scale multi-modal
globally distributed benchmark dataset consisting of satellite images
with more than 5000 patches on landslide detection.

The first three participants with the highest F1 scores will be
introduced as winners. In addition, allowing competition participants
to provide innovative ideas more freely without being limited to a
clear numerical metric, two more selected submissions will be awarded
the special prizes. The ranking of these two submissions is based on
the evaluation of the methodological descriptions of the introduced
method by the Landslide4Sense competition committee as well as
international expert reviewers.

Please check the competition website to find out more information on
the dataset and the competition deadlines:


The workshop is a part of IJCAI-ECAI 2022 conference. The conference
venue is Messe Wien Exhibition and Congress Center, which is one of
the most modern exhibition and conference centres.

Messe Wien
Hall B, entrance Congress Center
Messeplatz 1
A-1020 Vienna
Metro stop U2 Messe Prater

Please find more information about the conference venue here:


Organising Committee
Pedram Ghamisi, Helmholtz-Zentrum Dresden-Rossendorf, Germany and Institute of Advanced Research in Artificial Intelligence, Austria
Aleksandra Gruca, Silesian University of Technology, Poland
Naoto Yokoya, University of Tokyo, Japan; RIKEN Center for Advanced Intelligence Project, Japan
Jun Zhou, Griffith University, Australia
Caleb Robinson, Microsoft AI for Good Research Lab, Redmond, USA
Fabio Pacifici, Maxar Technologies
Pierre-Philippe Mathieu, European Space Agency F-lab, Italy
Sepp Hochreiter, Institute of Advanced Research in Artificial Intelligence, Austria

Steering Committee
Pedram Ghamisi, Helmholtz-Zentrum Dresden-Rossendorf, Germany and Institute of Advanced Research in Artificial Intelligence, Austria
Ioannis Giannopoulos, Technical University of Vienna Austria
Michael Kopp, Institute of Advanced Research in Artificial Intelligence, Switzerland
David Kreil, Institute of Advanced Research in Artificial Intelligence, Austria

Programme Committee
Shizhen Chang, Institute of Advanced Research in Artificial Intelligence, Austria
Leyuan Fang, Hunan University, China
Omid Ghorbanzadeh, Institute of Advanced Research in Artificial Intelligence, Austria
Wei He, Wuhan University, China
Danfeng Hong, Aerospace Information Research Institute, CAS, China
Andrzej Kucik, European Space Agency, Italy
Manil Maskey, National Aeronautics and Space Administration, USA
Claudio Pressello, University of Twente, The Netherlands
Behnood Rasti, HZDR, Germany
Bertrand Le Saux, European Space Agency F-lab, Italy
Rochelle Schneider, European Space Agency F-lab, Italy
Rongjun Qin, The Ohio State University, USA
Junshi Xia, RIKEN, Japan
Martin Werner, Technical University of Munich, Germany
Fengchao Xiong, Nanjing University of Science and Technology, China
Yonghao Xu, Institute of Advanced Research in Artificial Intelligence, Austria