mlsanityData to 7 Oct 2026

Interactive Mars Image Content-Based Search with Interpretable Machine Learning

arXiv 2402.16860 ↗First posted 19 Jan 2024cs.CV cs.IRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 25 references that are in the map. First posted on arXiv 19 Jan 2024.

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15 of 25 references.

References · 15

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3 more
  1. 2020iGOS++: Integrated Gradient Optimized Saliency by Bilateral Perturbationscited by 19
  2. 2021Mars Image Content Classification: Three Years of NASA Deployment and Recent Advancescited by 10
  3. 2022Visual Explanations from Deep Networks via Riemann-Stieltjes Integrated Gradient-based Localizationcited by 5

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