  The increasing number of RSOs has raised concerns about the risk of
collisions and catastrophic incidents for all direct and indirect users of
space. To mitigate this issue, it is essential to have a good understanding of
the various RSOs in orbit and their behaviour. A well-established taxonomy
defining several classes of RSOs is a critical step in achieving this
understanding. This taxonomy helps assign objects to specific categories based
on their main characteristics, leading to better tracking services.
Furthermore, a well-established taxonomy can facilitate research and analysis
processes by providing a common language and framework for better understanding
the factors that influence RSO behaviour in space. These factors, in turn, help
design more efficient and effective strategies for space traffic management.
Our work proposes a new taxonomy for RSOs focusing on the low Earth orbit
regime to enhance space traffic management. In addition, we present a deep
learning-based model that uses an autoencoder architecture to reduce the
features representing the characteristics of the RSOs. The autoencoder
generates a lower-dimensional space representation that is then explored using
techniques such as Uniform Manifold Approximation and Projection to identify
fundamental clusters of RSOs based on their unique characteristics. This
approach captures the complex and non-linear relationships between the features
and the RSOs' classes identified. Our proposed taxonomy and model offer a
significant contribution to the ongoing efforts to mitigate the overall risks
posed by the increasing number of RSOs in orbit.
