1st Workshop on Complex Data Challenges in Earth Observation affiliated with ACM International Conference on Information and Knowledge Management - CIKM 2021 l virtual

Workshop URL: https://www.iarai.ac.at/events/workshop-on-complex-data-challenges-in-earth-observation/

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IMPORTANT DATES

Submission opens: 14.06.2021
Submission deadline: 15.07.2021 (AoE)
Paper acceptance notifications: 15.08.2021
Camera ready submissions: 23.08.2021 (AoE)
Workshop dates: 1.11.2021

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ABOUT THE WORKSHOP

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. Advanced EO sensors nowadays generate rich streams of data around the clock. An effective interpretation of the resulting complex large-scale data adopts the best machine learning techniques from signal processing, computer vision, pattern recognition, and artificial intelligence (AI).
In this workshop we aim to bring together a range of domain experts from remote sensing, geographic information systems and computer vision, researchers in the field of weather and climate modeling, as well as other scientists or engineers with a general interest of application of modern data analysis methods in the EO domain.
The focus of the workshop is to advance research in Earth observation which will form the basis for effectively addressing urgent social challenges affected by changes in our environment such as natural catastrophes or climate change.

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SCOPE AND TOPICS

The CDCEO workshop invites both method development and advanced applications in a wide range of related topics, including image and signal processing, gap-filling, data fusion, feature extraction, prediction of spatio-temporal features, and the detection of rules underlying the observed state transitions and causal relationships. The topics covered by the workshop theme include but are not limited to:

* Spatio-temporal data processing and analysis
* Multi-resolution, multi-temporal, multi-sensor, and multi-modal data fusion
* Machine learning for weather and climate research
* Deep learning and its applications to, e.g., semantic segmentation, scene classification, and feature extraction
* Advanced applications of time-series data analysis, e.g., urban sprawl, deforestation, crop monitoring, weather forecasts
* Feature extraction, feature selection, and dimensionality reduction
* Meta learning, including transfer learning, few-shot learning, and active learning
* Data acquisition and efficient pre-processing of diverse remote sensing measurements including:
** Passive sensor images (panchromatic, multispectral, and hyperspectral)
** Active sensor data (LiDAR, RADAR, and SAR)
* Integration and aggregation of complementary remote sensing measurements
* Advances in signal processing with applications to, e.g., unmixing, denoising
* Benchmark datasets with application to Earth Observation

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SUBMISSION INFORMATION

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.
Workshop papers will be included in a CIKM companion volume published by http://ceur-ws.org/. Papers must be formatted in CEUR style guidelines and be submitted via dedicated submission link (to be provided later). 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 proceedings.

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SPECIAL ISSUE

Authors of the accepted papers will be invited to extend their work and submit it for a special issue in a JCR-indexed journal.

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WREATHER4CAST COMPETITION

Weather forecasts are of obvious immediate value, but also are an important part of EO, informing about continuing changes of our environment. Modern ML methods have recently become viable alternatives to long standing physics-based forecasting solutions.

A special session of the workshop will present the winning solutions and highlights from a unique multi-sensor weather forecasting competition. The goal of the competition is a short-term prediction of the selected weather products based on meteorological satellites data obtained in collaboration with AEMET/ NWC SAF. The competition data are presented in a form of weather movies that consist of multi-channel images encoding the cloud properties, temperature, turbulence, and rainfall.

Join the competition via weather4cast.ai!

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ORGANISERS:

Steering Committee:
Pedram Ghamisi, Institute of Advanced Research in Artificial Intelligence, Austria
Antonio Plaza, University of Extremadura, Spain
Liangpei Zhang, Wuhan University, China

Programme Committee:
Leyuan Fang, Hunan University, China
Omid Ghorbanzadeh, University of Salzburg, Austria; Institute of Advanced Research in Artificial Intelligence, Austria
Danfeng Hong, German Aerospace Center, Germany
Andrea Marinoni, UiT the Arctic University of Norway, Norway
Claudio Persello, University of Twente, The Netherlands
Behnood Rasti, Helmholtz-Zentrum Dresden-Rossendorf, Germany
Martin Werner, Technical University of Munich, Germany
Yonghao Xu, Wuhan University, China; Institute of Advanced Research in Artificial Intelligence, Austria
Shizhen Zhang, Wuhan University, China; Institute of Advanced Research in Artificial Intelligence, Austria
Jun Zhou, Griffith University, Australia

Organizing Committee:
Aleksandra Gruca, Institute of Advanced Research in Artificial Intelligence, Austria
Pedro Herruzo, Institute of Advanced Research in Artificial Intelligence, Austria
Pilar Rípodas, Spanish Meteorological Agency, Spain
Pedram Ghamisi, Institute of Advanced Research in Artificial Intelligence, Austria
Christian Briese, Earth Observation Data Centre for Water Resources Monitoring
Andrzej Kucik, European Space Agency Centre for Earth Observation, Italy
Michael Kopp, Institute of Advanced Research in Artificial Intelligence, Austria
David Kreil, Institute of Advanced Research in Artificial Intelligence, Austria
Sepp Hochreiter, Institute of Advanced Research in Artificial Intelligence, Austria