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Website:</div>
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<div class="ContentPasted0 elementToProof">https://aiforspace.github.io/2024/</div>
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<div class="ContentPasted0">Call for Papers:</div>
<div class="ContentPasted0 elementToProof">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.</div>
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<div class="ContentPasted0 elementToProof ContentPasted1">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).
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<div class="ContentPasted1">A specific list of topics is as follows:</div>
<div class="ContentPasted1">- Vision and learning for spacecraft navigation and operations (e.g., rendezvous, proximity operations, docking, space maneuvers, entry descent landing).</div>
<div class="ContentPasted1">- Vision and learning for space robots (e.g., rovers, UAVs, UGVs, UUWs) and multi-agent systems.</div>
<div class="ContentPasted1"><span style="display: inline !important; background-color: rgb(255, 255, 255);" class="ContentPasted2">- </span>Mapping and global positioning on planetary bodies (moon, Mars, asteroids), including celestial positioning.</div>
<div class="ContentPasted1"><span style="display: inline !important; background-color: rgb(255, 255, 255);" class="ContentPasted3">- </span>Onboard AI for Earth observation applications (e.g. near-real-time disaster monitoring, distributed learning on satellites,
tip and cue satellite-based systems).</div>
<div class="ContentPasted1"><span style="display: inline !important; background-color: rgb(255, 255, 255);" class="ContentPasted4">- </span>Onboard AI for satellite operations (e.g. AI-based star trackers, fault detection isolation and recovery).</div>
<div class="ContentPasted1"><span style="display: inline !important; background-color: rgb(255, 255, 255);" class="ContentPasted5">- </span>Space debris monitoring and mitigation.</div>
<div class="ContentPasted1"><span style="display: inline !important; background-color: rgb(255, 255, 255);" class="ContentPasted6">- </span>Sensors for space applications (e.g., optical, multispectral, lidar, radar, neuromorphic).</div>
<div class="ContentPasted1"><span style="display: inline !important; background-color: rgb(255, 255, 255);" class="ContentPasted7">- </span>Onboard compute hardware for vision and learning (e.g., neural network accelerators, neuromorphic processors).</div>
<div class="ContentPasted1"><span style="display: inline !important; background-color: rgb(255, 255, 255);" class="ContentPasted8">- </span>Mitigating challenges of the space environment (e.g., radiation, thermal) to vision and learning systems.</div>
<span style="display: inline !important; background-color: rgb(255, 255, 255);" class="ContentPasted9">- </span>Datasets, transfer learning and domain gap.<br>
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<div class="ContentPasted0">Paper deadline:</div>
<div class="ContentPasted0 elementToProof">1 March 2024</div>
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<div class="ContentPasted0 elementToProof">The workshop will also feature exciting Invited Seminars by Soon-Jo Chung (Caltech and JPL) and Gianluca Furano (ESA).</div>
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<div class="ContentPasted0 elementToProof">More details:</div>
<div class="ContentPasted0 elementToProof ContentPasted10"><a href="https://aiforspace.github.io/2024/" id="LPNoLPOWALinkPreview_3" data-loopstyle="linkonly" class="OWAAutoLink">https://aiforspace.github.io/2024/</a></div>
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