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As the demand for software to support the processing and analysis of massiveradio astronomy datasets increases in the era of the SKA, we demonstrate theinteractive workflow building, data mining, processing, and visualisationcapabilities of DUG Insight. We test the performance and flexibility of DUGInsight by processing almost 68,000 full sky radio images produced from theEngineering Development Array (EDA2) over the course of a three day period. Thegoal of the processing was to passively detect and identify known ResidentSpace Objects (RSOs: satellites and debris in orbit) and investigate how radiointerferometry could be used to passively monitor aircraft traffic. Thesesignals are observable due to both terrestrial FM radio signals reflected backto Earth and out-of-band transmission from RSOs. This surveillance of the lowEarth orbit and airspace environment is useful as a contribution to spacesituational awareness and aircraft tracking technology. From the observations,we made 40 detections of 19 unique RSOs within a range of 1,500 km from theEDA2. This is a significant improvement on a previously published study of thesame dataset and showcases the flexible features of DUG Insight that allow theprocessing of complex datasets at scale. Future enhancements of our DUG Insightworkflow will aim to realise real-time acquisition, detect unknown RSOs, andcontinue to process data from SKA-relevant facilities.

DUG Insight: A software package for big-data analysis and visualisation,  and its demonstration for passive radar space situational awareness using  radio telescopes