Kelvins SpotGEO Challenge

The Geostationary orbit (GEO) is home to many important space assets such as telecommunication and navigational satellites. Monitoring Resident Space Objects (RSOs) in GEO is a crucial aspect in achieving Space Situational Awareness (SSA) and in protecting critical space assets. However, ground-based GEO object detection is challenging due to the extreme distance of the targets, as well as nuisance factors including cloud coverage, atmospheric/weather effects, light pollution, sensor noise/defects, and star occlusions. The Kelvins SpotGEO Challenge is designed to establish to what extent images coming from a low-cost ground-based telescope can be used to detect GEO and near-GEO RSOs solely from image sequences that do not carry any additional meta-data. At the same time, the SpotGEO dataset also addresses the lack of publicly available datasets from a computer vision perspective on the satellite detection problem; by assembling and releasing such a dataset, we hope to spur more efforts on the optical detection of RSOs and enable objective benchmarking for existing and future methods. For more detals about this challenge, visit https://kelvins.esa.int/spot-the-geo-satellites/home/.

Bo Chen
Bo Chen
Research Fellow

My research interests lie in the intersection of machine learning and computer vision.