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Website:</div>
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<div style="margin:0px" class="elementToProof"><a href="https://aiforspace.github.io/2022/" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="0" style="margin:0px">https://aiforspace.github.io/2022/</a></div>
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<div style="margin:0px">Call for Papers:</div>
<div style="margin:0px">We solicit papers for AI4Space. Papers will be reviewed and accepted papers will be published in the proceedings of ECCV Workshops. Authors of accepted papers will also be invited to present at the workshop (in hybrid mode) at ECCV 2022,
Tel-Aviv, late Oct 2022.</div>
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<div style="margin:0px">The general emphasis of AI4Space is vision and learning algorithms in off-Earth environments, including in the orbital region, surface and underground environments on other planetary bodies (e.g., the moon, Mars and asteroids), interplanetary
space and solar system, and distant galaxies. Target application areas include autonomous spacecraft, space robotics, space traffic management, astronomy, astrobiology and cosmology. Emphasis is also placed on novel sensors and processing hardware for vision
and learning in space, mitigating the challenges of the space environment towards vision and learning (e.g., solar radiation, extreme temperatures), and solving practical difficulties in vision and learning for space (e.g., lack of training data, unknown or
partially known characteristics of operating environments).</div>
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<div style="margin:0px">A specific list of topics is as follows:</div>
<div style="margin:0px">- Visual navigation for spacecraft operations</div>
<div style="margin:0px">- Vision and learning for space robotics</div>
<div style="margin:0px">- GPS-denied positioning on the moon and Mars</div>
<div style="margin:0px">- Space debris monitoring and mitigation</div>
<div style="margin:0px">- Vision and learning for astronomy, astrobiology and cosmology</div>
<div style="margin:0px">- Novel sensors for space applications</div>
<div style="margin:0px">- Processing hardware for vision and learning in space</div>
<div style="margin:0px">- Mitigating challenges of the space environment to vision and learning</div>
<div style="margin:0px">- Datasets, transfer learning and domain gap for space problems</div>
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<div style="margin:0px">Paper deadline:</div>
<div style="margin:0px" class="elementToProof">11:59pm 15 July 2022<br>
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<div style="margin:0px">More details:</div>
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<span class="x_elementToProof" style="font-size:15px;font-family:Arial, Helvetica, sans-serif;margin:0px;color:rgb(0, 0, 0) !important;background-color:rgb(255, 255, 255)"><a href="https://aiforspace.github.io/2022/" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="1" style="margin:0px">https://aiforspace.github.io/2022/</a></span></div>
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