[In the Journal]
Authors:
- Neuraspace, Portugal, {marta.guimaraes, chiara.manfletti}@neuraspace.com
- FCT-UNL, Portugal, claudia.soares@fct.unl.pt
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.
Download the paper to learn more about:
- 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