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Taxonomy for Resident Space Objects in LEO: A Deep Learning Approach

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[In the Journal]


  • Neuraspace, Portugal, {marta.guimaraes, chiara.manfletti}
  • FCT-UNL, Portugal,

Having a well-established taxonomy of Resident Space Objects (RSOs) enables assigning objects to specific categories, leading to better tracking services, thereby achieving a better understanding of the RSOs. This will in turn help design efficient and effective strategies for Space Traffic Management.

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  • The increasing number of RSOs and its implications for Space Sustainability
  • Data sources used
  • Deep learning model using autoencoder architecture
  • Latent space visualization and clustering
  • Extracting insights from clusters using the Decision Tree-based model, and
  • Taxonomy

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