mlsanityData to 7 Oct 2026

On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery

arXiv 2106.15281 ↗First posted 29 Jun 2021cs.CV cs.LG eess.IVcited by 10

Not on the map. Why?

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 6 of its 47 references that are in the map. First posted on arXiv 29 Jun 2021.

Builds on

6 of 47 references.

References · 6

  1. 2019Recent Advances in Deep Learning for Object Detectioncited by 50
  2. 2019A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasetscited by 13
  3. 2020Automatic Dataset Builder for Machine Learning Applications to Satellite Imagerycited by 7
  4. 2020A New Mask R-CNN Based Method for Improved Landslide Detectioncited by 7
  5. 2019Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Datacited by 2
  6. 2018Detecting Volcano Deformation in InSAR using Deep learningcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 3; 2026: 2.

10 papers.

  1. 2023Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligencecited by 14
  2. 2022Selected Trends in Artificial Intelligence for Space Applicationscited by 5
  3. 2025Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networkscited by 4
  4. 2024AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 missioncited by 3
  5. 2024Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiationcited by 3
  6. 2025FPGA-Based Neural Network Accelerators for Space Applications: A Surveycited by 2
  7. 2024Benchmarking of a new data splitting method on volcanic eruption datacited by 0
  8. 2025Transforming volcanic monitoring: A dataset and benchmark for onboard volcano activity detectioncited by 0
  9. 2026Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AIcited by 0
  10. 2026AquaCubeAI-Powered Monitoring Turbidity on-board Phisat-2cited by 0