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arXiv Computer Vision By Pranav Durai, Gary Doran

MANTLE: A Framework for Adaptive In-Situ Planetary Perception Using a Modular Uplink Principle

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MANTLE is a multi‑task adaptive network designed for planetary perception, featuring a shared DINOv2 backbone with separate heads for landform classification and boulder segmentation. Trained on HiRISE and MSL imagery, it achieved 92.56% accuracy on seven Martian terrain classes and a 0.753 IoU for boulder segmentation, with strong cross‑sol generalization. The framework follows the Modular Uplink Principle, allowing lightweight task‑specific heads to be trained on Earth and uplinked to the rover without retraining the core model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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