ML for space debris detection (joint project with ESA)

Supervisors

Suitable for

Abstract

In-orbit collisions with debris endanger astronauts, end missions, and generate new fragments. Databases with orbital information are needed to avoid such collisions. Debris and satellites in high altitude orbits are typically observed from ground-based telescopes or from space-based optical payloads. The smaller the objects, the less light is reflected from the sun and therefore only a faint signal is received at the sensor. Due to the relative motion, the object will then create a streak-like feature in the image. A recent ESA-funded activity has resulted in the creation of a database with several thousand trailed images of objects of interest. However, every image has typically not only the streak-like feature of the object of interest, but also presents some other streaks. This project aims at detecting such streaks using machine learning. That is, identifying which of a known set of objects might be present and provide information about their positions within the image. The labelled dataset is currently under active development at ESA/ESOC and will be provided to the student under an NDA. The student will then develop either an active learning approach or an unsupervised learning approach.

Prerequisites: only suitable for someone who has worked in Machine Learning in the past (computer vision), and has strong programming skills (Python).