We are a student-led project dedicated to tracking satellites and space debris using ground-based telescopes.
Founded at EPFL in 2020, the SSA EPFL Team is a student-led initiative of around 20 students passionate about Space Situational Awareness (SSA). We are working on multiple projects that aim to address challenges in orbital sustainability, debris monitoring, and space safety. For more details, visit the Projects page to explore our reports and ongoing efforts. Through research, practical projects, and outreach, we are aiming to raise awareness about the space environment and contribute to innovation in the growing field of SSA.
We currently have two high-quality telescopes that we use for observing satellites and debris, both from the EPFL campus and occasionally from more remote locations. Our equipment includes the Rowe-Ackermann Schmidt Astrograph 36, an excellent choice for astrophotography with its wide field of view, nicknamed LOST (Low Orbit Satellite Tracker). Additionally, we have a more portable telescope, the TS-Optics Hypergraph 130, aptly named POET (Portable Optical Exploration Tool). These are complemented by advanced cameras and specialized astrophotography equipment, enabling us to capture detailed observations and data.
L.O.S.T. - Celestron RASA 360/790
The problem of space debris is growing exponentially. It has becomes a real and tangible threat to space missions. Since the first launches, space has been considered as an infinite environment, but the reality has set in as collisions have become more and more frequent. A first step in addressing the issue of space debris is knowing where they are, and where they’re going to be !
Geostationnary satellites EUTELSAT Konnect, 7C and 7B.
Geostationnary satellites EUTELSAT Konnect, 7C and 7B.
Our goal is to autonomously gather data using our cutting-edge telescope. To achieve this, we work on building a cupola to protect it from the elements, with a first prototype already installed on the roof of the Cubotron at EPFL. We’re also working on integrating a weather station combined with professional meteorological data to optimize observation times.
In the near future, we will set up the telescope in a remote location to gather the best data possible. This will require an independent energy source and a solid remote connection to send our data.
Using a large database of existing satellite images taken from the Earth, the SSA EPFL Team will train a neural network to detect, classify and identify orbiting objects. A catalog containing relevant information about each object will then be made public and continuously updated with every satellite launch or debris detect. This catalog with be made public and accessible by any entity that wishes to use it.