AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor
  Shower Mapping
Siddha Ganju Amartya Hatua Peter Jenniskens Sahyadri Krishna Chicheng Ren Surya Ambardar
The Cameras for Allsky Meteor Surveillance (CAMS) project, funded by NASA
starting in 2010, aims to map our meteor showers by triangulating meteor
trajectories detected in low-light video cameras from multiple locations across
16 countries in both the northern and southern hemispheres. Its mission is to
validate, discover, and predict the upcoming returns of meteor showers. Our
research aimed to streamline the data processing by implementing an automated
cloud-based AI-enabled pipeline and improve the data visualization to improve
the rate of discoveries by involving the public in monitoring the meteor
detections. This article describes the process of automating the data
ingestion, processing, and insight generation using an interpretable Active
Learning and AI pipeline. This work also describes the development of an
interactive web portal (the NASA Meteor Shower portal) to facilitate the
visualization of meteor radiant maps. To date, CAMS has discovered over 200 new
meteor showers and has validated dozens of previously reported showers.