---
title: "Taxonomy for Resident Space Objects in LEO: A Deep Learning Approach"
description: 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
image: https://blog.neuraspace.com/hubfs/LinkedIn_Neura%20(1).png
---

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 Dec 5, 2023, 11:20:33 AM

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

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- 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

### Download the paper

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