Applications of artificial intelligence have been gaining extraordinary traction in recent years across innumerable domains. These novel approaches and technological leaps permit leveraging profound quantities of data in a manner from which to elucidate and ease the modeling of arduous physical phenomena. ExoAnalytic collects over 500,000 resident space object images nightly with an arsenal of over 300 autonomous sensors; extending the autonomy of collection to data curation, anomaly detection, and notification is of paramount importance if elusive events are desired to be captured and classified. Efforts begin with rigorous image annotation of observed glints, streaking stars, and resident space objects with plumes from debris shedding events. Preliminary results permitted the successful classification of observed debris generating events from AMC-9, Telkom-1, and Intelsat-29e. After initial proof-of-concept, these events are incorporated into the training pipeline in order to characterize potentially unknown debris generating or anomalous events in future observations. The inclusion of a visual tracking system aides in reducing false alarms by roughly 30%. Future efforts include applications on both historical datamining as well as real-time indications and warnings for satellite analysts in their daily operations while maintaining a low probability of false alarm through detection and tracking algorithm refinement.

近年来，人工智能的应用已在无数领域中获得了极大的关注。这些新颖的方法和技术飞跃允许以阐明和简化艰苦的物理现象建模的方式利用大量数据。ExoAnalytic每晚通过300多个自主传感器的武库收集超过500,000个居民空间物体图像。如果希望捕获和分类难以捉摸的事件，则将收集的自主权扩展到数据管理，异常检测和通知至关重要。努力始于对观察到的闪光，裸奔的恒星和居民空间物体进行严格的图像注释，这些碎片带有来自碎片脱落事件的羽流。初步结果可以成功地对观察到的来自AMC-9，Telkom-1和Intelsat-29e的碎片生成事件进行分类。在最初的概念验证之后，这些事件将合并到训练流水线中，以便在将来的观测中表征潜在的未知碎片生成或异常事件。包含视觉跟踪系统有助于将虚假警报减少大约30％。未来的工作包括在历史数据挖掘以及卫星分析师日常操作中的实时指示和警告方面的应用，同时通过检测和跟踪算法的改进保持虚假警报的可能性很小。这些事件被合并到训练流水线中，以便在将来的观测中表征潜在的未知碎片生成或异常事件。包含视觉跟踪系统有助于将虚假警报减少大约30％。未来的工作包括在历史数据挖掘以及卫星分析师日常操作中的实时指示和警告方面的应用，同时通过检测和跟踪算法的改进保持虚假警报的可能性很小。这些事件被合并到训练流水线中，以便在将来的观测中表征潜在的未知碎片生成或异常事件。包含视觉跟踪系统有助于将虚假警报减少大约30％。未来的工作包括在历史数据挖掘以及卫星分析师日常操作中的实时指示和警告方面的应用，同时通过检测和跟踪算法的改进保持虚假警报的可能性很小。