A transfer learning approach to space debris classification using observational light curve data

This paper presents a data driven approach to space object characterisation through the application of machine learning techniques to observational light curve data. One-dimensional convolutional neural networks are shown to be effective at classifying the shape of objects from both simulated and real light curve data. To the best of the authors’ knowledge this is the first generalised attempt to classify the shape of space objects using real observational light curve data.