Jin-Hyung Kim

Papers

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About

Jin-Hyung Kim is a researcher specializing in space object recognition, deep learning applications in aerospace engineering, and vision-based satellite servicing systems. His work sits at the intersection of artificial intelligence and space technology, focusing on the increasingly critical challenge of on-orbit space object classification — a field of growing importance as the proliferation of nanosatellites reshapes modern space operations. Kim's most notable contribution, "Strategy for on-orbit space object classification using deep learning" (2021), addresses the complex problem of identifying and categorizing space objects during rendezvous operations. By leveraging deep learning methodologies applied to vision-based sensor data, his research provides a practical framework for enabling autonomous satellite servicing missions — a capability essential for the next generation of space robotics. This work has garnered 7 citations since its publication, reflecting its relevance within the specialized community of space situational awareness and on-orbit servicing research. Kim's research is particularly timely given the global surge in nanosatellite deployment, where verification platforms demand robust, intelligent classification systems. His contributions help bridge the gap between advanced machine learning techniques and the demanding operational realities of space environments, offering valuable insights for researchers and engineers working toward safer, more autonomous space infrastructure.