# Demonstrating Onboard Inference for Earth Science Applications with Spectral Analysis Algorithms and Deep Learning

arXiv: https://arxiv.org/abs/2508.15053 · HTML: https://mlsanity.com/paper/2508.15053/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 1 of 1)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)

## Built on by (most cited first, 3 of 3)

- [Real-Time Instrument Planning and Perception for Novel Measurements of Dynamic Phenomena](https://mlsanity.com/paper/2509.03500/) (2025)
- [First On-Orbit Demonstration of a Geospatial Foundation Model](https://mlsanity.com/paper/2512.01181/) (2025)
- [Dynamic Targeting of Satellite Observations Using Supplemental Geostationary Satellite Data and Hierarchical Planning](https://mlsanity.com/paper/2603.06719/) (2026)

## Abstract (arXiv)

In partnership with Ubotica Technologies, the Jet Propulsion Laboratory is demonstrating state-of-the-art data analysis onboard CogniSAT-6/HAMMER (CS-6). CS-6 is a satellite with a visible and near infrared range hyperspectral instrument and neural network acceleration hardware. Performing data analysis at the edge (e.g. onboard) can enable new Earth science measurements and responses. We will demonstrate data analysis and inference onboard CS-6 for numerous applications using deep learning and spectral analysis algorithms.