2026-10-11T00:25:59Z https://www.scidb.cn/oai

10.57760/sciencedb.28448 2025-10-10T14:04:31Z

2025-10-10 Neuromorphic Vision-Based Dataset for Space Situational Awareness Applications doi:10.57760/sciencedb.28448 en The space technology sector witnesses the increasing density of space debris and defunct satellites in Earth orbit. Space debris poses growing risks to future space missions and operations that intensified the need for advanced Space Situational Awareness (SSA) technologies. Neuromorphic vision sensors (NVS) offer a potential alternative to conventional optical and radar ground systems, providing microsecond temporal resolution, high dynamic range, and high altitude observations. In this work, we present a new labeled dataset for real-time detection and tracking of Resident Space Objects (RSOs). The data is acquired using two neuromorphic sensors DAVIS346 and DVXplorer640 integrated with a high-precision 0.8 m aperture Ritchey–Chrétien telescope. The dataset includes event-based recordings of 15 satellites and space debris, along with stars of varying brightness for performance benchmarking. The data is annotated to support supervised training of deep learning models. This dataset is a foundation for developing high-performance RSO detection AI algorithms, such as flash attention-based networks. Neuromorphic Vision; Space Situational Awareness; Space Imaging; Artificial Intelligence Hazem Elrefaei Afnan Ahmed Adil Yusra Alkendi Konstantin Kravtsov Sana Amairi-Pyka Anton Ivanov Yahya Zweiri PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank