[ASA] Deep Vision in Space - IJCNN 2023

[Karl Glazebrook]

Dear ASA

I’d like to bring this QLD conference in June to the attention of the Australian astronomical community - the special session ‘Deep Vision in Space’ is highly relevant, astronomy is a topic of interest and there are excellent interdisciplinary and industry networking opportunities.

Karl


_______________________
Prof. Karl Glazebrook FAA FASA
Laureate Fellow & Distinguished Professor
JWST Australian Data Centre<https://protect-au.mimecast.com/s/gFNTCvl1rKiW11j9mTQ0ilT?domain=jadc.swin.edu.au>,
Centre for Astrophysics & Supercomputing, Swinburne University of Technology
Contact: +61-3-9214-4384  kglazebrook at swin.edu.au<mailto:kglazebrook at swin.edu.au>
https://protect-au.mimecast.com/s/U6xNCwV1vMfL66zjMCqRrB_?domain=astronomy.swin.edu.au<https://protect-au.mimecast.com/s/yNXLCxngwOfJ884l6sYoGjM?domain=astronomy.swin.edu.au> @karlglazebrook
About me: https://protect-au.mimecast.com/s/gaj3CyojxQTNDDz1vTRsLFE?domain=science.org.au


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CALL FOR PAPERS
2023 International Joint Conference on Neural Networks (IJCNN 2023)<https://protect-au.mimecast.com/s/rwFoCzvkyVCRDDp1lsoGRrz?domain=2023.ijcnn.org>
Gold Coast, Queensland, Australia, June 18-23, 2023
Special Session “Deep Vision in Space”<https://protect-au.mimecast.com/s/DWZNCANpgjC9003QyFMEZnf?domain=alexkaiqin.org>
Aim and Scope

Modern AI and advanced sensing technologies have been transforming our ability to monitor the Earth and explore the Universe. By analysing and interpreting data (primarily in the form of imagery) captured by remote sensing devices (like multi/hyper‐spectral imaging or radar/lidar sensors) on satellites, aircrafts or UAVs and astronomical telescopes that operate either on ground or in orbit, valuable insights can be gained into the events on the Earth and the phenomena in the Universe.

Recent years have witnessed rapid advances in remote sensing technologies, resulting in an explosive growth of Earth observation data for probing the entire Earth at daily or even finer granularity. On the other hand, many new astronomical telescopes with enhanced sensing capabilities, like the recently launched James Webb Space Telescope, have been put into operation, generating massive data about the never explored aspects of the Universe. Nowadays, thanks to the boom of modern AI techniques, particularly deep learning, armed by an unprecedented growth in supercomputing power, such space data can be transformed into valuable scientific discoveries and actionable insights which may benefit various fields, such as astronomy, transportation, agriculture, and environment. However, the rapidly increasing complexity and requirements of newly emerging applications in different fields are posing greater challenges to existing AI techniques, leading to the surging needs of technology advancement.

This special session aims to bring together researchers from academia, governments and industries to review past achievements, disseminate latest studies, and explore future directions pertaining to innovating and applying modern AI techniques, particularly deep learning, to analyse space data. Authors are invited to submit original and unpublished works with topics including but not limited to:

  *   Deep vision for observing the Earth

     *   Remote sensing data collection and curation, with data captured by various types of active (e.g., radar and lidar) and passive (e.g., optical) sensors on satellites, aircrafts, UAVs, etc.
     *   Deep vision techniques for remote sensing data processing analysis and interpretation, including but not limited to:

        *   Image denoising, restoration, and super‐resolution
        *   Image registration, segmentation, classification and retrieval
        *   Object/event detection, recognition, and tracking
        *   Change detection
        *   Feature engineering (e.g., selection and extraction) and representation learning
        *   Data fusion and compression
        *   Advanced machine learning techniques (e.g., transfer, federated, self‐supervised, semi‐supervised, few‐shot, and adversarial learning)
        *   Physics‐informed neural networks
        *   Onboard machine learning, deep learning, and computer vision
        *   Edge AI platforms, frameworks, and techniques
        *   Security and privacy

     *   Earth observation applications including but not limited to transportation, urban design, agriculture, energy, environment, and management of resources and emergency

  *   Deep vision for probing other planets such as the Moon and the Mars
  *   Deep vision for exploring the Universe

     *   Astronomical data collection and curation, with data captured by various telescopes that operate either on land or in orbit
     *   Deep vision techniques for astronomical data processing, analysis and interpretation

        *   Image denoising, restoration, and super‐resolution
        *   Image segmentation, classification and retrieval
        *   Object detection and recognition
        *   Unknown (“anomaly”) detection
        *   Feature engineering (e.g., selection and extraction) and representation learning
        *   Advanced machine learning techniques (e.g., transfer, federated, self‐supervised, semi‐supervised, few‐shot, and adversarial learning)
        *   Physics‐informed neural networks
        *   Onboard machine learning, deep learning, and computer vision
        *   Edge AI platforms, frameworks, and techniques

     *   Universe exploration applications including but not limited to gravitational lens detection, photo-z estimation, star‐formation history estimation, etc.

  *   Deep vision driven intelligent decision‐making in space

     *   Swarm intelligence for satellite constellation
     *   Onboard event‐driven decision‐making in space
     *   Collective intelligence for mission‐critical applications

  *   Responsible AI in space


Important Dates

•         Paper submission deadline:                   January 31, 2023 (possibly to be extended)

•         Paper decision notification date:           March 31, 2023
Please refer to https://protect-au.mimecast.com/s/mXZ6CBNqjlCVqq9GQHvuH0J?domain=2023.ijcnn.org for the latest date information.

Paper Submission
All papers should be submitted electronically through: https://protect-au.mimecast.com/s/pQM-CD1vlpTBppK92IBs60f?domain=2023.ijcnn.org
NOTE: When you submit your papers to our special session, please select "Special Session: Deep Vision in Space" as your s<https://protect-au.mimecast.com/s/DgtACE8wmrtWyyJ7rTyYag-?domain=edas.info>ubmission portal<https://protect-au.mimecast.com/s/DgtACE8wmrtWyyJ7rTyYag-?domain=edas.info> (shown below).
<image001.jpg><https://protect-au.mimecast.com/s/DgtACE8wmrtWyyJ7rTyYag-?domain=edas.info>

Special Session Co-Chairs
Kai Qin
Department of Computing Technologies, Swinburne University of Technology, Australia
Email: kqin at swin.edu.au<mailto:kqin at swin.edu.au>
Yuan-Sen Ting
School of Astronomy & Astrophysics, Australian National University, Australia
Email: yuan-sen.ting at anu.edu.au<mailto:yuan-sen.ting at anu.edu.au>
Avik Bhattacharya
Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, India
Email: avikb at csre.iitb.ac.in<mailto:avikb at csre.iitb.ac.in>

Program Committee
•        Prof. Benjamin D. Wandelt, Institut Astrophysique de Paris, France
•        Dr. Bertrand Le Saux, Φ-lab, European Space Agency, Italy
•        Prof. Clinton Fookes, Queensland University of Technology, Australia
•        Prof. David A. Clausi, University of Waterloo, Canada
•        Prof. Elif Sertel, Istanbul Technical University, Turkey
•        Dr. Gemine Vivone, National Research Council, Italy
•        Assoc. Prof. Ingo Waldmann, University College London, UK
•        Dr. Jack White, EY Australia, Australia
•        Dr. Jasmine Muir, Symbios, Australia
•        Prof. Jocelyn Chanussot, Grenoble Institute of Technology, France
•        Dr. Justin Alsing, Stockholm University and Calda AI, Sweden
•        Prof. Karl Glazebrook, Swinburne University of Technology, Australia
•        Prof. Maoguo Gong, Xidian University, China
•        Assoc. Prof. Marc Huertas-Company, Université de Paris, France
•        Dr. Nicolas Longépé, Φ-lab, European Space Agency, Italy
•        Prof. Plamen Angelov, Lancaster University, UK
•        Dr. Ronny Hänsch, German Aerospace Center, Germany
•        Assoc. Prof. Saurabh Prasad, University of Houston, USA
•        Prof. Sébastien Lefèvre, University of South Brittany, France
•        Prof. Yang Gao, University of Surrey, UK
This special session is supported by IEEE Computational Intelligence Society (CIS) Neural Networks Technical Committee (NNTC)<https://protect-au.mimecast.com/s/HleCCGv0oyCJnnp7xsWcPPo?domain=cis.ieee.org> Task Force "Deep Vision in Space<https://protect-au.mimecast.com/s/MVZdCJyBrGf8OOmJGFnzJLO?domain=alexkaiqin.org>".
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