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Onboard AI is gaining interest for space applications such as vessel, wildfire, and cloud detection, where real-time processing can improve mission reactivity and reduce downlink needs. However, onboard models may operate on raw or minimally processed imagery rather than on restored ground products. This study evaluate...
Large-scale Digital Surface Models (DSMs) can be produced cost-effectively from satellite images via stereo-photogrammetry. However, the resulting 3D maps are often contaminated by noise, outliers, and voids. On the other hand, aerial LiDAR provides high-accuracy elevation measurements at a substantially higher cost. I...
TriCCOT is introduced, a tri-part architecture for robust and deployable onboard object detection that combines a convolutional region proposal network, a conformal prediction stage, and Aper-GATES, the authors' hardware-friendly attention-based classifier, enabling unified CNN-Transformer inference for spaceborne embe...
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