Interactive Mars Image Content-Based Search with Interpretable Machine Learning

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.

Published at the Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24). Corrected citation: Proc. AAAI 38(21): 22976-22982 (2024)

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