Space Situational Awareness (SSA) involves detection, tracking, and forecasting the movements of objects in Earth's orbit. It is crucial for protecting space assets and preventing collisions, yet challenging due to the vast number of objects, their high-velocity interactions, and difficult lighting conditions. In this project, we propose using a Neuromorphic Vision Sensor (NVS) to observe and detect Resident Space Objects (RSOs) to advance SSA field. The NVS is an innovative, bio-inspired sensor that asynchronously triggers logarithmic light intensity changes at the pixel level. The sensor will be mounted on a high-end 0.8 m diameter Ritchey–Chrétien telescope, part of the Abu Dhabi Quantum Optical Ground Station (ADQOGS) equipped with less than 8” pointing accuracy. This NVS event camera is activity-dependent, resulting in significantly less data for sparse scenes compared to traditional cameras. Furthermore, the NVS can capture high-speed motion and fine details of RSOs, overcoming the limitations of conventional cameras that suffer from motion blur and low resolution. By exploiting the unique characteristics of the NVS, we aim to develop a novel and robust approach for RSO detection that can handle various scenarios and lighting conditions. Coupled with reduced power consumption, lower processing requirements, wider dynamic ranges, and faster communication speeds, these attributes make NVS exceptionally well-suited for space imaging and SSA applications. We are developing an innovative deep learning algorithm to process NVS event stream observations, enabling it to differentiate between event streams related to the sky background and those related to RSOs. The algorithm processes and reveals the spatial-temporal correlations between events in the NVS data and outputs the event labels. The algorithm is trained on publicly available NVS-based observations, and initial evaluations have shown promising results, demonstrating the potential of Artificial Intelligence (AI) for processing NVS raw data in SSA applications. Moving forward, we aim to contribute to the advancement of space imaging technology while ensuring the reliability and validity of our research findings. The deliverables include the experimental setup equipping NVS sensor to a local ground telescope and calibration, the advanced deep learning algorithms for RSO detection, tracking, and classification and the creation of a new NVS-based dataset recorded locally in the UAE. These efforts are poised to substantially elevate the UAE's capabilities in RSO monitoring, contributing to enhanced accuracy, efficiency, and operational insights, thereby strengthening space situational awareness and the overall management and research of space objects.