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arXiv · 2402.16860

Interactive Mars Image Content-Based Search with Interpretable Machine Learning

Bhavan Vasu·Steven Lu·Emily Dunkel·Kiri L. Wagstaff·Kevin Grimes·Michael McAuley
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Abstract

The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions. The ever-expanding nature of data and user engagement demands an interpretable content classification system to support scientific discovery and individual curiosity. In this paper, we leverage a prototype-based architecture to enable users to understand and validate the evidence used by a classifier trained on images from the Mars Science Laboratory (MSL) Curiosity rover mission. In addition to providing explanations, we investigate the diversity and correctness of evidence used by the content-based classifier. The work presented in this paper will be deployed on the PDS Image Atlas, replacing its non-interpretable counterpart.

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Bhavan Vasu, Steven Lu, Emily Dunkel, Kiri L. Wagstaff, Kevin Grimes, Michael McAuley. 2024-01-19. Interactive Mars Image Content-Based Search with Interpretable Machine Learning. https://doi.org/10.1609/aaai.v38i21.30338

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