# Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit

arXiv: https://arxiv.org/abs/2305.04059 · HTML: https://mlsanity.com/paper/2305.04059/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 6 May 2023.

## Builds on (most cited first, 7 of 7)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [On-Board Federated Learning for Dense LEO Constellations](https://mlsanity.com/paper/2111.12769/) (2021)
- [Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2206.00307/) (2022)
- [MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels](https://mlsanity.com/paper/2103.10368/) (2021)
- [Laser Inter-Satellite Link Setup Delay: Quantification, Impact, and Tolerable Value](https://mlsanity.com/paper/2301.05285/) (2023)
- [PAseos Simulates the Environment for Operating multiple Spacecraft](https://mlsanity.com/paper/2302.02659/) (2023)

## Built on by (most cited first, 4 of 4)

- [Scheduling for On-Board Federated Learning with Satellite Clusters](https://mlsanity.com/paper/2402.09105/) (2024)
- [AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning](https://mlsanity.com/paper/2505.03509/) (2025)
- [Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellites](https://mlsanity.com/paper/2411.17831/) (2024)
- [Label Less, Learn More: Resource-Efficient Active Semi-Supervised Learning for Onboard Satellite Image Annotation](https://mlsanity.com/paper/2609.37481/) (2026)

## Abstract (arXiv)

Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs. We showcase the training of a machine learning model on a satellite constellation for scene classification using semi-supervised learning while accounting for operational constraints such as temperature and limited power budgets based on satellite processor benchmarks of the neural network. We evaluate mission scenarios employing both decentralised and federated learning approaches. All scenarios achieve convergence to high accuracy (around 91% on EuroSAT RGB dataset) within a one-day mission timeframe.