# Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances

arXiv: https://arxiv.org/abs/2102.05011 · HTML: https://mlsanity.com/paper/2102.05011/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)

## Built on by (most cited first, 10 of 10)

- [Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images](https://mlsanity.com/paper/2204.09854/) (2022)
- [ConeQuest: A Benchmark for Cone Segmentation on Mars](https://mlsanity.com/paper/2311.08657/) (2023)
- [MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications](https://mlsanity.com/paper/2604.02719/) (2026)
- [Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations](https://mlsanity.com/paper/2201.05775/) (2022)
- [Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars](https://mlsanity.com/paper/2207.05679/) (2022)
- [Improving Contrastive Learning on Visually Homogeneous Mars Rover Images](https://mlsanity.com/paper/2210.09234/) (2022)
- [Enhancing Martian Terrain Recognition with Deep Constrained Clustering](https://mlsanity.com/paper/2503.17633/) (2025)
- [Natural Language-Driven Global Mapping of Martian Landforms](https://mlsanity.com/paper/2601.15949/) (2026)
- [Mixed-domain Training Improves Multi-Mission Terrain Segmentation](https://mlsanity.com/paper/2209.13674/) (2022)
- [Interactive Mars Image Content-Based Search with Interpretable Machine Learning](https://mlsanity.com/paper/2402.16860/) (2024)

## Abstract (arXiv)

The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed content-based classification and search capabilities for Mars orbital and surface images. The deployed systems are publicly accessible using the PDS Image Atlas. We describe the process of training, evaluating, calibrating, and deploying updates to two CNN classifiers for images collected by Mars missions. We also report on three years of deployment including usage statistics, lessons learned, and plans for the future.