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.
Cite
Text
Vasu et al. "Interactive Mars Image Content-Based Search with Interpretable Machine Learning." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30338Markdown
[Vasu et al. "Interactive Mars Image Content-Based Search with Interpretable Machine Learning." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/vasu2024aaai-interactive/) doi:10.1609/AAAI.V38I21.30338BibTeX
@inproceedings{vasu2024aaai-interactive,
title = {{Interactive Mars Image Content-Based Search with Interpretable Machine Learning}},
author = {Vasu, Bhavan and Lu, Steven and Dunkel, Emily and Wagstaff, Kiri L. and Grimes, Kevin and McAuley, Michael},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {22976-22982},
doi = {10.1609/AAAI.V38I21.30338},
url = {https://mlanthology.org/aaai/2024/vasu2024aaai-interactive/}
}