EventSat: A CubeSat Mission for Neuromorphic Vision-Based Space Object Detection
R. M. García Alarcia, S. Dolan, J. Sindermann, C. J. Juan Oliver, L. Schuberth, V. Messina, F. Salmaso and A. Golkar
Proceedings of the 5th Symposium on Space Educational Activities (SSEA 2026), Technical University of Munich, 2026
Abstract
EventSat is a student-driven mission aimed at demonstrating space-based detection, classification, and identification of objects by leveraging neuromorphic vision. The mission consists of a 6U CubeSat with commercial-grade subsystems and a payload comprising an event camera and a commercial-off-the-shelf embedded AI computer. As part of the mission, algorithms are developed for event compression and object detection, taking into account the camera’s very low latency and the specific characteristics of event data. The data may inform space objects catalogues for Space Situational Awareness (SSA). The EventSat mission serves as a platform for both academic and educational activities, allowing for the research and development of novel vision technologies and algorithms for their application in space, as well as providing training opportunities for students through hands-on learning with real space hardware. In this paper, we provide insights into the student-based structure of the team and the educational projects associated with it, with a focus on the impact on student learning through their involvement in the mission.
Keywords: Event camera, CubeSat, satellite mission, educational mission
Cite
@inproceedings{GarciaAlarcia2026EventSatCubeSat,
author = {Garc{\'\i}a Alarcia, Ram{\'o}n Mar{\'\i}a and Dolan, Sydney and Sindermann, Jaspar and Juan Oliver, Clemente Javier and Schuberth, Lara and Messina, Vincenzo and Salmaso, Francesco and Golkar, Alessandro},
title = {{EventSat}: A {CubeSat} Mission for Neuromorphic Vision-Based Space Object Detection},
booktitle = {Proceedings of the 5th Symposium on Space Educational Activities (SSEA 2026)},
address = {Munich, Germany},
publisher = {Technical University of Munich},
month = {apr},
year = {2026},
doi = {10.14459/2026md1857638},
url = {https://mediatum.ub.tum.de/1857638},
abstract = {EventSat is a student-driven mission aimed at demonstrating space-based detection, classification, and identification of objects by leveraging neuromorphic vision. The mission consists of a 6U CubeSat with commercial-grade subsystems and a payload comprising an event camera and a commercial-off-the-shelf embedded AI computer. As part of the mission, algorithms are developed for event compression and object detection, taking into account the camera's very low latency and the specific characteristics of event data. The data may inform space objects catalogues for Space Situational Awareness (SSA). The EventSat mission serves as a platform for both academic and educational activities, allowing for the research and development of novel vision technologies and algorithms for their application in space, as well as providing training opportunities for students through hands-on learning with real space hardware. In this paper, we provide insights into the student-based structure of the team and the educational projects associated with it, with a focus on the impact on student learning through their involvement in the mission.},
keywords = {Event camera, CubeSat, satellite mission, educational mission},
file = {2026/2026_GarciaAlarcia_EventSatCubeSatMissionNeuromorphicVision.pdf}
}