January 4th, 2024 
Daniela Lopez de Luise Website:
Call for Papers:
We solicit papers for AI4Space. Papers will be reviewed and accepted papers will be published in the proceedings of CVPR Workshops. Accepted papers will also be presented at the workshop, to be co-located with CVPR 2024 in Seattle.
The general emphasis of AI4Space is vision and learning algorithms for autonomous space systems, which operate in the Earth’s orbital regions, cislunar orbit, planetary bodies (e.g., the moon, Mars, and asteroids), and interplanetary space. Emphasis is also placed on novel sensors and processors for vision and learning in space, mitigating the challenges of the space environment towards vision and learning (e.g., radiation, extreme temperatures), and fundamental difficulties in vision and learning for space (e.g., lack of training data, unknown operating environments).
A specific list of topics is as follows:
- Vision and learning for spacecraft navigation and operations (e.g., rendezvous, proximity operations, docking, space maneuvers, entry descent landing).
- Vision and learning for space robots (e.g., rovers, UAVs, UGVs, UUWs) and multi-agent systems.
- Mapping and global positioning on planetary bodies (moon, Mars, asteroids), including celestial positioning.
- Onboard AI for Earth observation applications (e.g. near-real-time disaster monitoring, distributed learning on satellites, tip and cue satellite-based systems).
- Onboard AI for satellite operations (e.g. AI-based star trackers, fault detection isolation and recovery).
- Space debris monitoring and mitigation.
- Sensors for space applications (e.g., optical, multispectral, lidar, radar, neuromorphic).
- Onboard compute hardware for vision and learning (e.g., neural network accelerators, neuromorphic processors).
- Mitigating challenges of the space environment (e.g., radiation, thermal) to vision and learning systems.
- Datasets, transfer learning and domain gap.
Paper deadline: 1 March 2024
The workshop will also feature exciting Invited Seminars by Soon-Jo Chung (Caltech and JPL) and Gianluca Furano (ESA).
More details:
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