A “Practical Data Science” Approach to Detecting Meteors with CAMS

From Medium. com, January 27, 2022

Have you ever looked up to a starry night sky, seen a shooting star and made a wish? I am sure you have. Well, look again and look carefully. Are you sure it is a shooting star, or could it be something else? Can you tell for sure? Well, maybe if your wish comes true, then you can tell with certainty that it was a shooting star, no? 🙂

This Fall semester at New College of Florida, 7 students* in the Applied Data Science master’s program joined the world-wide effort in analyzing data collected from cameras watching the night skies. Known as the CAMS project (Cameras for Allsky Meteor Surveillance), a NASA-sponsored initiative with the SETI Institute and under the leadership of Dr. Peter Jenniskens, the effort is aimed at detecting meteors in the night sky through the use of data collected from multiple cameras watching night-time sky activity. Since 2011, 19 locations worldwide have been launched to collect such data that are intended to help astronomers and researchers to detect meteors, calculate their orbit trajectories and trace their origins. Many researchers and data scientists have tackled to date the challenging task of analyzing these large datasets with the help of data science and machine learning techniques. But this process wasn’t always easy, in fact scientists had to travel to the camera locations, grab the video data, come back to their offices and spend hours in almost uneventful darkness watching the video footage to recognize meteors. in 2017 a team from Frontier Development Lab (FDL) automated this process and built an initial data processing pipeline. Since then the CAMS AI pipeline has been improved yearly, leading to 6x growth and now in 2021 a team from SpaceML improved the AI models precision and recall and built a portal to view meteor data from all over the world (more information here).

Read more here.