Nested Fusion: A Method for Learning High Resolution Latent Structure of Multi-Scale Measurement Data on Mars

TL;DR

Nested Fusion is a novel method that integrates multi-scale, multi-resolution measurements from Mars rover instruments to produce high-resolution latent representations, enhancing exploratory data analysis for planetary science.

Contribution

The paper introduces Nested Fusion, a new technique for combining layered datasets of different resolutions to generate detailed latent structures, improving analysis of Mars rover data.

Findings

Outperforms existing dimensionality reduction methods on Mars data

Efficiently handles large, multi-resolution datasets

Deployed successfully within NASA's Mars science team

Abstract

The Mars Perseverance Rover represents a generational change in the scale of measurements that can be taken on Mars, however this increased resolution introduces new challenges for techniques in exploratory data analysis. The multiple different instruments on the rover each measures specific properties of interest to scientists, so analyzing how underlying phenomena affect multiple different instruments together is important to understand the full picture. However each instrument has a unique resolution, making the mapping between overlapping layers of data non-trivial. In this work, we introduce Nested Fusion, a method to combine arbitrarily layered datasets of different resolutions and produce a latent distribution at the highest possible resolution, encoding complex interrelationships between different measurements and scales. Our method is efficient for large datasets, can perform inference even on unseen data, and outperforms existing methods of dimensionality reduction and latent analysis on real-world Mars rover data. We have deployed our method Nested Fusion within a Mars science team at NASA Jet Propulsion Laboratory (JPL) and through multiple rounds of participatory design enabled greatly enhanced exploratory analysis workflows for real scientists. To ensure the reproducibility of our work we have open sourced our code on GitHub at https://github.com/pixlise/NestedFusion.