On Board Artificial Intelligence: A New Era for Earth Observation Satellites
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 38785
Special Issue Editors
Interests: earth observation; CubeSats; NewSpace; earth science remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: healthcare and telemedicine
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
AI is a general-purpose technology already transforming the global economy but still largely untapped potential for Earth Observation (EO) technology. As described in the “AI for the Earth” report presented at the World Economic Forum in 2018, AI research is the “new electricity” fuelling the fourth Industrial revolution. In the past, the main innovation in EO were derived by AI applications on ground data leveraging on large scale computing capability e.g. Cloud computing or GPU architectures.
Thanks on the advances in microelectronics of space grade AI hardware accelerator, today we have the possibility to exploit AI directly on board opening a new era for EO satellites where feature extraction and decision making is performed directly on-board thus reducing unnecessary data exchanged between satellite sensors and ground.
Exploiting this “back to the edge” paradigm, EO could obtain huge advantages increasing satellite operativity and improving capabilities and performance.
In particular the current Special Issue invites contributions on innovative model concepts or improvements of existing AI/ML techniques for space missions, promoting new on-board architectures, or sensors that allow to improve the science of Earth observation through the use of the new hardware accelerators directly on-board AI, such as Intel Movidius Myriad-2 or space-grade FPGA.
Potential topics for this Special Issue include but are not limited to the following:
- High-resolution multi-classes segmentation on-board
- High performances CNN on-board
- Recurrent network on-board
- Disaster detection through AI on-board satellite (fire, hurricane, flooding, etc.)
- Vehicle detection through AI on-board satellite (plane, vessel, boat, etc.)
- Generative network for dataset creation
- ML model reduction/quantization techniques
Dr. Massimiliano Pastena
Prof. Dr. Luca Fanucci
Guest Editors
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Keywords
- AI4EO
- On board data processing
- Machine learning for remote sensing
- GAN for EO
- Quantization method for ML
- Innovative remote sensing technique
- On-line information extraction
- Edge computing
- HPC
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