Welcome to the 1st workshop on Complex Data Challenges in Earth Observation (CDCEO) 2021. This workshop is held as a satellite event at the 30th ACM International Conference on Information and Knowledge Management (CIKM). CIKM 2021 runs as a fully virtual conference.

The workshop focuses on advancing research in Earth observation (EO) by effectively interpreting the high-dimensional heterogeneous data obtained by high-resolution remote sensing technologies.

Big data accumulated by ground, aerial, and satellite-based remote sensors at an unprecedented scale and resolution invite the application of modern data-hungry machine learning (ML) methods.

The workshop aims to bring together researchers in the fields of remote sensing, geographic information systems, weather and climate modeling, computer vision, and others with a general interest in applying data-driven models in EO.

Workshop Topics

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

The workshop topics include but are not limited to:

  • spatiotemporal data processing and analysis;

  • multi-resolution, multi-temporal, multi-sensor, and multi-modal data fusion;

  • ML 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 EO.

Important Dates

  • Submission opens: 14.06.2021
  • Submission deadline: 29.07.2021 (Anywhere on Earth) (extended)
  • Paper acceptance notifications: 20.08.2021 (extended)
  • Camera ready submissions: 28.08.2021 (Anywhere on Earth) (extended)
  • Poster presentation recording submission: 17.10.2021 CET
  • Oral presentation recording submission (optional): 24.10.2021 CET
  • Workshop registration deadline: 25.10.2021 AoE
  • Workshop date:  01.11.2021.

Invited Speakers

Xavier Calbet

Nowcasting Sattelite Application Facility, Spanish Meteorological Agency, Spain

Application of Artificial Intelligence/Machine Learning techniques in Remote Sensing for Meteorology

Abstract:
Meteorology is a field that traditionally has had big data volumes. With the advent of meteorological satellites than routinely observe the Earth, these data volumes have increased considerably. This tendency will grow even more as the satellite technology to observe the Earth evolves. As an example, the new generation of geostationary satellites that EUMETSAT will launch in the near future known as Meteosat Third Generation (MTG) will have instruments with increased spatial, temporal and channel resolution. The Flexible Combined Imager (FCI) will measure in 16 spectral channels and between 0.5 and 2 km spatial resolution measuring every 2.5 to 10 minutes. The InfraRed Sounder (IRS) will measure with a spatial resolution of 4 km every 60 minutes at around 2000 different channels at different wavelengths in the infrared. The total data rate will be increased more than 10-fold with respect to the previous Meteosat generation (Meteosat Second Generation).

Given these huge data volumes in satellite meteorology, it is clear that this field can benefit greatly from Machine Learning/Artificial Intelligence (ML/AI) techniques. One clear application is the diagnosis of the current state of the atmosphere from a global perspective using these instrument in combination with ML/AI. Another obvious candidate is the forecast of the weather from a few minutes to hours of lead time (Nowcasting) to several days (Weather Forecasting).

Even though these applications of ML/AI are quite obvious, it is not straightforward to apply them in practice. It must always be considered that several factors need to exist in order for these techniques to be successful. On one hand, the information we are trying to extract should be there in the original data. A careful analysis of the input data needs to be made to verify this. On the other hand, the problem at hand must be mathematically tractable by these techniques. In this presentation examples of several applications that can or cannot succeed will be given.

Bio:
Science coordinator for the EUMETSAT Nowcasting Satellite Application Facility (NWC SAF) and the State Meteorological Agency of the Government of Spain AEMET. He has worked with EUMETSAT in Darmstadt specializing in atmospheric profile retrievals obtained from hyper-spectral sounding instruments, such as the Infrared Atmospheric Sounding Interferometer, and planning for the future geostationary hyperspectral sounding mission MTG-IRS. He has also worked as a state meteorologist at AEMET.

Peter Dueben

European Centre for Medium-Range Weather Forecasts (ECMWF)

Machine Learning for Weather and Climate Predictions

Abstract: 
The talk provides an overview of the work on machine learning methods that is ongoing at the European Centre for Medium-Range Weather Forecasts, and outlines how machine learning, and in particular deep learning, could help to improve weather predictions in the coming years. The talk will name challenges for the use of machine learning and suggest developments (research/software/hardware) that should enable the community of Earth system modelling to make quick progress.

Bio:
Peter is the AI and Machine Learning Coordinator at ECMWF and holds a University Research Fellowship of the Royal Society that enables him to perform research towards the use of machine learning, high-performance computing, and reduced numerical precision in weather and climate predictions. Peter has also a strong interest in the quantification of uncertainty of predictions for chaotic systems. Peter is coordinator of the MAELSTROM EuroHPC-Join Undertaking project, work-package leader of the ESiWACE2 H2020 project, and Co-Pi of an US-INCITE grant to perform season-long, global, storm-resolving simulations. Before moving to ECMWF, Peter has written his PhD thesis at the Max Planck Institute for Meteorology and has worked as PostDoc with Tim Palmer at the University of Oxford.

Wolfgang Wagner

Department of Geodesy and Geoinformation, Technical University Wien, Austria

Earth Observation Data Centre for Water Resources Monitoring, Austria

Advancing the Understanding of Spaceborne Radar Observations using Machine Learning and Physical Models

Abstract:
Earth observation satellites equipped with radar sensors have become indispensable for monitoring the land surface from local to global scales. In particular, the Sentinel-1 satellites series represents a breakthrough as it acquires new radar imagery with a spatial resolution of 20 m every few days. Since the launch of the first Sentinel-1 satellite in 2014, several Petabyte of data have already been collected. Given the large data volume and the difficulties in the interpretation of the radar data, machine learning (ML) techniques are increasingly used alongside physical models to derive geophysical data sets such as soil moisture, water bodies, or forest type. In this contribution, I review a number of studies that have used ML techniques such as Support Vector Regression, Gradient Boosted Regression Trees, or Long-Short Term Memory for the classification and interpretation of Sentinel-1 radar data. The success of all these studies was routed in the availability of high quality reference data for ML training and validation. The ML models often worked surprisingly well, however, sometimes for the wrong reasons. In addition, the limits of the applicability of the trained models are often not clear. Therefore, the usefulness of ML for turning radar measurements into higher-value data sets depends critically on the context and intended application of the data. Further improvements in our understanding of spaceborne radar data can be expected from the combination of physical models and ML approaches.

Bio:
Wolfgang Wagner received the Dipl.-Ing. (MSc) degree in physics and the Dr.techn. (PhD) degree in remote sensing from the Technische Universität Wien (TU Wien), Austria, in 1995 and 1999 respectively. In support of his master and PhD studies he received fellowships to carry out research at the University of Bern (1993), Atmospheric Environment Service Canada (1994), NASA Goddard Space Flight Centre (1995), European Space Agency (1996), and the Joint Research Centre of the European Commission (1996-1998). From 1999 to 2001 he was with the German Aerospace Agency (DLR). In 2001 he was appointed professor for remote sensing at the Institute of Photogrammetry and Remote Sensing of TU Wien. From 2006 to 2011 he was the head of the Institute of Photogrammetry and Remote Sensing, from 2012 to 2019 the head of the newly founded Department of Geodesy and Geoinformation, and since 2020 he serves as the dean of the Faculty for Mathematics and Geoinformation. Furthermore, he is co-founder of the EODC Earth Observation Data Centre, where he has worked part-time as senior scientist since December 2014. Since 2018 he has also been affiliated with the Global Change Research Institute of the Czech Academy of Sciences (CzechGlobe).

Program

Video Title of the talk Speaker Affiliation
Video 1st session – November 1st, 12:00 – 1:15 PM Chair: Pedram Ghamisi
Invited talk: Advancing the Understanding of Spaceborne Radar Observations using Machine Learning and Physical Models Wolfgang Wanger  Department of Geodesy and Geoinformation, TU Wien, Austria Earth Observation Data Centre for Water Resources Monitoring, Austria
Towards Very-Low Latency Storm Nowcasting through AI-Based On-Board Satellite Data Processing Robert Hinz DEIMOS Space S.L.U, Spain
Uncertainty-aware Graph-based Multimodal Remote Sensing Detection of Out-of-distribution Samples Iain Rolland University of Cambridge, United Kingdom
Towards Geographical Aware Neural Networks for Geospatial Vector Data: A Case Study on Land Use and Land Cover Classification Marvin Mc Cutchan Institute of Advanced Research in Artificial Intelligence, Austria TU Wien, Austria
COFFEE BREAK (Join us at the IARAI topia)
Video 2nd session – November 1st, 1:30 – 2:45 PM Chair:  Jun Zhou
Invited talk: Machine Learning for Weather and Climate Predictions Peter Dueben  European Centre for Medium-Range Weather Forecasts (ECMWF)
Region-Growing Fully Convolutional Networks for Hyperspectral Image Classification with Point-Level Supervision Yonghao Xu Wuhan University, China
Hyperspectral Anomaly Detection based on Low-rank Structure Exploration Shizhen Chang Wuhan University, China
On the Exploitation of Heterophily in Graph-based Multimodal Remote Sensing Data Analysis Catherine Taelman UiT – The Arctic University of Norway, Norway
COFFEE BREAK (Join us at the IARAI topia)
Poster Session – November 1st, 3:00 – 4:00 PM
Graph Neural Sparsification for Hyperspectral Image Classification with Local and Global Consistency  Haojie Hu Xi’an Research Institute of High Technology, China
Video Hyperspectral Denoising: From Conventional techniques Towards Deep Learning ones Behnood Rasti Alexander von Humboldt Research Fellow, Helmholtz Institute Freiberg for Resource Technology, Germany
Video Point-based Weakly Supervised Deep Learning for Water Extraction from High-resolution Remote Sensing Imagery Ming Lu Hunan University, China
Video Machine Learning Model Development for Space Weather Forecast Randa Natras Technical University of Munich, Germany
Large-scale Hyperspectral Image Clustering Using Contrastive Learning Yaoming Cai China University of Geosciences, China
Video FORCE on Nextflow: Scalable Analysis of Earth Observation data on Commodity Clusters Fabian Lehmann Institute for Computer Science, Humboldt-Universität zu Berlin, Germany
Change Detection for Hyperspectral Imagery based on Multi-layer Cascade Screening Strategy Lian Liu Aerospace Information Research Institute, Chinese Academy of Sciences, China
Video End-to-end CNN-CRFs for Multi-date Crop Classification Using Multitemporal Remote Sensing Image Sequences Laura Elena Cué La Rosa Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil
COFFEE BREAK (Join us at the IARAI topia)
Video Special Weather4cast Competition Session – November 1st, 4:15 – 6:00 PM Chair: David Kreil
Invited talk: Application of Artificial Intelligence/Machine Learning techniques in Remote Sensing for Meteorology Xavier Calbet  Spanish Meteorological Agency AEMET, Spain
The Weather4cast Stage1 Competition Design and Data Pedro Herruzo Institute of Advanced Research in Artificial Intelligence, Austria
1st prize: Spatiotemporal Weather Data Predictions with Shortcut Recurrent-Convolutional Networks: A Solution for the Weather4cast challenge Jussi Leinonen Federal Office of Meteorology and Climatology  MeteoSwiss, Switzerland
2nd prize: Utilizing UNet for the future weather prediction: Weather4cast 2021’ Sungbin Choi Republic of Korea
3rd prize: A Variational U-Net for Weather Forecasting Pak Hay Kwok, Qi Qi United Kingdom
Efficient Spatio-temporal Weather Forecasting with Deep Neural Networks Akshay Punjabi, Pablo Izquierdo Ayala Spain
Spatiotemporal Swin-Transformer Network for Short Time Weather Forecasting Alabi Bojesomo Khalifa University, UAE
Closing remarks Pedram Ghamisi Institute of Advanced Research in Artificial Intelligence, Austria
Get-together (Join us at the IARAI topia)
VideoTitle of the talkSpeakerAffiliation

Video
1st session – November 1st, 12:00 – 1:15 PM | Chair: Pedram Ghamisi  
 Invited talk: Advancing the Understanding of Spaceborne Radar Observations using Machine Learning and Physical Models

by Wolfgang Wanger
Wolfgang Wanger Department of Geodesy and Geoinformation, TU Wien, Austria
Earth Observation Data Centre for Water Resources Monitoring, Austria
 Towards Very-Low Latency Storm Nowcasting through AI-Based On-Board Satellite Data Processing

by Robert Hinz
Robert HinzDEIMOS Space S.L.U, Spain
 Uncertainty-Aware Graph-Based Multimodal Remote Sensing Setection of Out-of-Distribution Samples

by Iain Rolland
Iain  RollandUniversity of Cambridge, UK
 Towards Geographical Aware Neural Networks for Geospatial Vector Data: A Case Study on Land Use and Land Cover ClassificationA

by Marvin Mc Cutchan
Marvin Mc Cutchan

Institute of Advanced Research in Artificial Intelligence, Austria

TU Wien, Austria

13:15 – 13:30COFFEE BREAK (Join us at the IARAI topia)  

Video
2nd session – November 1st, 1:30 – 2:45 PM  
13:30Invited talk: Machine Learning for Weather and Climate Predictions

by Peter Dueben
Peter Dueben European Centre for Medium-Range Weather Forecasts (ECMWF)
14:00Region-Growing Fully Convolutional Networks for Hyperspectral Image Classification with Point-Level Supervision

by Yonghao Xu
Yonghao XuWuhan University, China
14:15Hyperspectral Anomaly Detection Based on Low-Rank Structure Exploration

by Shizhen Cheng
Shizhen ChengWuhan University, China
14:30On the Exploitation of Heterophily in Graph-based Multimodal Remote Sensing Data Analysis

by Catherine Taelman
TBATBA
14:45 – 15:00COFFEE BREAK (Join us at the IARAI topia)  
Poster Session – November 1st, 3:00 – 4:00 PM  
 Graph Neural Sparsification for Hyperspectral Image Classification with Local and Global Consistency
by Haojie Hu
 Haojie HuXi’an Research Institute of High Technology, China
VideoHyperspectral Denoising: From Conventional techniques Towards Deep Learning ones
by Behnood Rasti
Behnood RastiAlexander von Humboldt Research Fellow, Helmholtz Institute Freiberg for Resource Technology, Germany
VideoPoint-based Weakly Supervised Deep Learning for Water Extraction from High-resolution Remote Sensing Imageryby Ming Lu
Ming LuHunan University, China
VideoMachine Learning Model Development for Space Weather Forecast
by Randa Natras
Randa NatrasTechnical University of Munich, Germany
 Large-Scale Hyperspectral Image Clustering Using Contrastive Learning
by Yaoming Cai
TBATBA
VideoFORCE on Nextflow: Scalable Analysis of Earth Observation data on Commodity Clusters
by Fabian Lehmann
Fabian LehmannInstitute for Computer Science, Humboldt-Universität zu Berlin, Germany
 Change Detection for Hyperspectral Imagery based on Multi-layer Cascade Screening Strategy
by Lian Liu
Lian LiuAerospace Information Research Institute, Chinese Academy of Sciences, China
VideoEnd-to-end CNN-CRFs for Multi-date Crop Classification Using Multitemporal Remote Sensing Image Sequences
by Laura Elena Cué La Rosa
Laura Elena Cué La RosaPontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil
 COFFEE BREAK (Join us at the IARAI)  
Special Weather4cast Competition Session – November 1st, 4:15 – 6:00 PM | Chair: David Kreil  
 Invited talk: Application of Artificial Intelligence/Machine Learning techniques in Remote Sensing for Meteorology

by Xavier Calbet
Xavier Calbet Spanish Meteorological Agency AEMET, Spain
 The Weather4cast Stage1 Competition Design and Data

by Pedro Herruzo
TBAInstitute of Advanced Research in Artificial Intelligence, Austria
 1st prize: Spatiotemporal Weather Data Predictions with Shortcut Recurrent-Convolutional Networks: A Solution for the Weather4cast challenge

by Jussi Leinonen
Jussi LeinonenFederal Office of Meteorology and Climatology  MeteoSwiss, Switzerland
 2nd prize: Utilizing UNet for the future weather prediction: Weather4cast 2021’

by Sungbin Choi
Sungbin Choi 
 3rd prize: A Variational U-Net for Weather Forecasting

by Pak Hay Kwok, Qi Qi
Pak Hay Kwok, Qi Qi 
 Efficient Spatio-temporal Weather Forecasting with Deep Neural Networks

by Akshay Punjabi, Pablo Izquierdo Ayala
Akshay Punjabi, Pablo Izquierdo Ayala 
 Spatiotemporal Swin-Transformer Network for Short Time Weather Forecasting

by Alabi Bojesomo
Alabi BojesomoKhalifa University, UAE
 Closing remarks

by Pedram Ghamisi
Pedram GhamisiInstitute of Advanced Research in Artificial Intelligence, Austria
 Get-together (Join us at the IARAI topia)  

Submission Instructions

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 preformed by members of Program Committee will be applied to select papers based on their novelty, technical quality, potential impact, clarity, and reproducibility.

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 EasyChair.  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.

Special issue

Authors of the accepted papers will be invited to extend their work and submit it for a special issue of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing journal.

A special session of the workshop will present the winning solutions and highlights from a unique multi-sensor weather forecasting competition. Join the competition via weather4cast.ai and predict weather products in various Earth regions!

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.

The goal of the competition is a short-term prediction of the selected weather products based on meteorological satellites data obtained in collaboration with AEMETNWC 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. The images are recorded at 15 minute intervals through the entire year in various Earth regions. Learn more… 

Steering Committee

Pedram Ghamisi

Institute of Advanced Research
in Artificial Intelligence, Austria

Helmholtz-Zentrum Dresden-Rossendorf, Germany

Antonio Plaza

University of Extremadura, Spain

Liangpei Zhang

Wuhan University, China

Program Committee

  • Shizhen Chang, Wuhan University, China; Institute of Advanced Research in Artificial Intelligence, Austria
  • 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 
  • Jun Zhou, Griffith University, Australia

Organizing Committee

Aleksandra
Gruca

Institute of Advanced Research in Artificial Intelligence, Austria

Silesian University of Technology, Poland

Pedro
Herruzo

Institute of Advanced Research in Artificial Intelligence, Austria

Pilar
Rípodas

Spanish Meteorological Agency, Spain

Andrzej
Kucik

European Space Agency
Φ-lab, Italy

Christian
Briese

Earth Observation Data Centre for Water Resources Monitoring, Austria

Pedram Ghamisi

Institute of Advanced Research in Artificial Intelligence, Austria

Helmholtz-Zentrum Dresden-Rossendorf, Germany

Michael
Kopp
Institute of Advanced Research in Artificial Intelligence, Austria
 
HereTechnologies, Switzerland
david_team
David
Kreil

Institute of Advanced Research in Artificial Intelligence, Austria

sepp_team
Sepp
Hochreiter
Institute of Advanced Research in Artificial Intelligence, Austria.

Institute of Advanced Research in Artificial Intelligence, Austria

European Space Agency
Φ-lab, Italy

 Spanish Meteorological Agency, Spain

Earth Observation Data Centre for Water Resources Monitoring, Austria

Contact

cdceo@iarai.ac.at

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