Acquiring information and knowledge about objects orbiting around earth is known as Space Situational Awareness (SSA). SSA has become an important research topic thanks to multiple large initiatives, e.g., from the European Space Agency (ESA), and from the American National Aeronautics and Space Administration (NASA).

Vision-based sensors are a great source of information for SSA, especially useful for spacecraft navigation and rendezvous operations where two satellites are to meet at the same orbit and perform close-proximity operations, e.g., docking, in-space refuelling, and satellite servicing. Moreover, vision-based target recognition is an important component of SSA and a crucial step towards reaching autonomy in space. However, although major advances have been made in image-based object recognition in general, very little has been tested or designed for the space environment.

State-of-art object recognition algorithms are deep learning approaches requiring large datasets for training. The lack of sufficient labelled space data has limited the efforts of the research community in developing data-driven space object recognition approaches. Indeed, in contrast to terrestrial applications, the quality of spaceborne imaging is highly dependent on many specific factors such as varying illumination conditions, low signal-to-noise ratio, and high contrast.

We propose a challenge to design data-driven approaches for space target recognition, including classification and detection. The proposed SPARK challenge launches a new unique space multi-modal annotated image dataset. The SPARK dataset contains a total of ∼150k RGB images and the same number, ∼150k, of depth images of 11 object classes, with 10 spacecrafts and one class of space debris.

The data have been generated under a realistic space simulation environment, with a large diversity in sensing conditions, including extreme and challenging ones for different orbital scenarios, background noise, low signal-to-noise ratio (SNR), and high image contrast that defines actual space imagery.

This challenge offers an opportunity to benchmark existing object classification and recognition algorithms, including multi-modal approaches using both RGB and depth data. It intends to intensify research efforts on automatic space object recognition and aims to spark collaborations between the image/vision sensing community and the space community.