# Adaptive and Robust Image Processing on CubeSats

arXiv: https://arxiv.org/abs/2506.03152 · HTML: https://mlsanity.com/paper/2506.03152/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 16 May 2025.

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

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Reaching the Edge of the Edge: Image Analysis in Space](https://mlsanity.com/paper/2301.04954/) (2023)

## Abstract (arXiv)

CubeSats offer a low-cost platform for space research, particularly for Earth observation. However, their resource-constrained nature and being in space, challenge the flexibility and complexity of the deployed image processing pipelines and their orchestration. This paper introduces two novel systems, DIPP and DISH, to address these challenges. DIPP is a modular and configurable image processing pipeline framework that allows for adaptability to changing mission goals even after deployment, while preserving robustness. DISH is a domain-specific language (DSL) and runtime system designed to schedule complex imaging workloads on low-power and memory-constrained processors. Our experiments demonstrate that DIPP's decomposition of the processing pipelines adds negligible overhead, while significantly reducing the network requirements of updating pipelines and being robust against erroneous module uploads. Furthermore, we compare DISH to Lua, a general purpose scripting language, and demonstrate its comparable expressiveness and lower memory requirement.