IEEE
CIS Neural Networks Technical Committee
IEEE
CIS Neural Networks Technical Committee
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Movitation
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 imagery) captured by remote sensing
devices (like multi/hyper‐spectral imaging or radar 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
histories of 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.
Goals
The goal of this task force is to provide a forum for the researchers from academia, governments and industries, who are conducting or interested in space data relevant research and applications, to make versatile collaborations on innovating and applying modern AI techniques, particularly deep learning, to analyse space data, primarily remote sensing and astronomical imagery, aiming to fully unleash the potential values of space data to benefit wider‐ranging fields.
Scope
The scope of this task force covers the following parts:
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
Chairs
Prof. Kai (Alex) Qin (Chair)
Swinburne University of Technology
Email: kqin@swin.edu.au
Assoc. Prof. Yuan-Sen Ting (Vice-Chair)
Australian National University
Email: yuan-sen.ting@anu.edu.au
Members
Prof. Avik Bhattacharya, Indian Institute of Technology Bombay, India
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
Activities
NEW! We are calling for papers for the Special Session "Deep Vision in Space" to be held at IJCNN 2023, Gold Coast, Australia, June 18-23, 2023
15-Nov-2022 We proposed a Special Session "Deep Vision in Space" at IJCNN 2023, Gold Coast, Australia, June 18-23, 2023
25-Oct-2022 Prof. Kai (Alex) Qin attended the 14th Australian Space Forum held in Adelaide, SA, Australia
15-Aug-2022 We organised a Special Issue on "Computational Intelligence for Remote Sensing Image Analysis and Applications”, MDPI Remote Sensing, 2022
01-Jun-2022 We organised a Special Issue (Topic) on "Computational Intelligence in Remote Sensing, MDPI Applied Sciences, Electronics, Mathematics, Remote Sensing, Algorithms, and AI
07-Apr-2022 Prof. Kai (Alex) Qin gave a talk "Deep Vision Towards the Earth and Universe" at the 2022 SmartSat CRC AI4Space Workshop, Brisbane, Australia, April 7, 2022
If you are interested in joining us or have any suggestions for us, please feel free to contact our chairs or members via email.