# On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery

arXiv: https://arxiv.org/abs/2106.15281 · HTML: https://mlsanity.com/paper/2106.15281/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Recent Advances in Deep Learning for Object Detection](https://mlsanity.com/paper/1908.03673/) (2019)
- [A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets](https://mlsanity.com/paper/1905.07286/) (2019)
- [Automatic Dataset Builder for Machine Learning Applications to Satellite Imagery](https://mlsanity.com/paper/2008.01578/) (2020)
- [A New Mask R-CNN Based Method for Improved Landslide Detection](https://mlsanity.com/paper/2010.01499/) (2020)
- [Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data](https://mlsanity.com/paper/1906.06151/) (2019)
- [Detecting Volcano Deformation in InSAR using Deep learning](https://mlsanity.com/paper/1803.00380/) (2018)

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

- [Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence](https://mlsanity.com/paper/2305.11891/) (2023)
- [Selected Trends in Artificial Intelligence for Space Applications](https://mlsanity.com/paper/2212.06662/) (2022)
- [Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks](https://mlsanity.com/paper/2506.04285/) (2025)
- [AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 mission](https://mlsanity.com/paper/2404.19586/) (2024)
- [Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation](https://mlsanity.com/paper/2405.02642/) (2024)
- [FPGA-Based Neural Network Accelerators for Space Applications: A Survey](https://mlsanity.com/paper/2504.16173/) (2025)
- [Benchmarking of a new data splitting method on volcanic eruption data](https://mlsanity.com/paper/2410.06306/) (2024)
- [Transforming volcanic monitoring: A dataset and benchmark for onboard volcano activity detection](https://mlsanity.com/paper/2510.22889/) (2025)
- [Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI](https://mlsanity.com/paper/2604.24492/) (2026)
- [AquaCubeAI-Powered Monitoring Turbidity on-board Phisat-2](https://mlsanity.com/paper/2609.12744/) (2026)

## Abstract (arXiv)

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study and a first prototype for an Artificial Intelligence (AI) model to be deployed on board satellites are presented in this work. As a case study, the detection of volcanic eruptions has been investigated as a method to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been proposed and designed, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements.