  This paper proposes the SPARK dataset as a new unique space object
multi-modal image dataset. Image-based object recognition is an important
component of Space Situational Awareness, especially for applications such as
on-orbit servicing, active debris removal, and satellite formation. However,
the lack of sufficient annotated space data has limited research efforts in
developing data-driven spacecraft recognition approaches. The SPARK dataset has
been generated under a realistic space simulation environment, with a large
diversity in sensing conditions for different orbital scenarios. It provides
about 150k images per modality, RGB and depth, and 11 classes for spacecrafts
and debris. This dataset offers an opportunity to benchmark and further develop
object recognition, classification and detection algorithms, as well as
multi-modal RGB-Depth approaches under space sensing conditions. Preliminary
experimental evaluation validates the relevance of the data, and highlights
interesting challenging scenarios specific to the space environment.
