Event-Based Vision in Space: Applications, Trends, and Future Directions
Not on the map. Why?
Abstract (arXiv)
Earth Observation (EO) is undergoing a significant transformation driven by the deployment of novel sensing technologies. Traditional frame-based optical sensors often struggle with motion blur, high power consumption, and extreme data redundancy in challenging orbital environments. In contrast, event-based sensors, also known as neuromorphic cameras, offer a bio-inspired asynchronous approach. By capturing only local illumination changes, they provide microsecond temporal resolution, an extremely high dynamic range, and exceptional energy efficiency. Although the use of these sensors is rapidly expanding from terrestrial systems to orbital platforms, the scientific literature surrounding their space-based applications remains heavily fragmented. To bridge this gap, this article presents a comprehensive review of the state-of-the-art in event-based vision in the space domain. Based on the retrieved literature, we introduce a taxonomy structured around four primary domains: 1) atmospheric and high-speed observation; 2) environmental monitoring and change detection; 3) operational support and onboard processing; and 4) geospatial modeling and predictive analysis. As a result, this survey highlights that neuromorphic engineering is far more than a supplementary imaging technique; it is a paradigm shift that can be used to directly address critical bottlenecks in modern remote sensing and sustainable space exploration.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 31 May 2026.
Builds on
23 of 50 references.
References · 23
- 2019Event-based Vision: A Surveycited by 1,010
- 2019High Speed and High Dynamic Range Video with an Event Cameracited by 344
- 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
- 2019End-to-End Learning of Representations for Asynchronous Event-Based Datacited by 216
- 2017Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarioscited by 150
- 2022Secrets of Event-Based Optical Flowcited by 92
- 2018Semi-Dense 3D Reconstruction with a Stereo Event Cameracited by 67
- 2024Recent Event Camera Innovations: A Surveycited by 67
- 2024SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networkscited by 38
- 2023Optical flow estimation from event-based cameras and spiking neural networkscited by 33
- 2023Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forwardcited by 28
- 2023Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardwarecited by 27
11 more
- 2023A Machine Learning-oriented Survey on Tiny Machine Learningcited by 26
- 2025Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solverscited by 10
- 2025Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellitescited by 6
- 2025Energy efficiency analysis of Spiking Neural Networks for space applicationscited by 6
- 2025Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networkscited by 5
- 2023Density Invariant Contrast Maximization for Neuromorphic Earth Observationscited by 4
- 2025Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networkscited by 4
- 2026Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Reviewcited by 4
- 2025Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Dronecited by 3
- 2026TinyML Enhances CubeSat Mission Capabilitiescited by 3
- 2025Event-based Star Tracking under Spacecraft Jitter: the e-STURT Datasetcited by 2
Built on by
Not cited yet.