Astronomers from the Dark Energy Survey rely on images of deep space to understand the nature of the universe, but these images are often polluted with “space junk”: asteroids, comets, satellites, or other objects from our own solar system obstructing the telescope’s view. In order to perform their analysis, scientists must first manually identify and mask out such objects from images, a time-consuming process. With leads Michael Troxel, Dan Scolnic, and Chris Walter, we’ve leveraged deep learning-based computer vision techniques to build models to automatically identify and localize space junk in deep space imagery.
Project Lead: Dan Scolnic, Michael Troxel, Chris Walter
