  Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting
understanding of geospatial scenarios from perception to cognition. In SAI,
objects exhibit great variations in scales and aspect ratios, and there exist
rich relationships between objects (even between spatially disjoint objects),
which makes it attractive to holistically conduct SGG in large-size
very-high-resolution (VHR) SAI. However, there lack such SGG datasets. Due to
the complexity of large-size SAI, mining triplets <subject, relationship,
object> heavily relies on long-range contextual reasoning. Consequently, SGG
models designed for small-size natural imagery are not directly applicable to
large-size SAI. This paper constructs a large-scale dataset for SGG in
large-size VHR SAI with image sizes ranging from 512 x 768 to 27,860 x 31,096
pixels, named STAR (Scene graph generaTion in lArge-size satellite imageRy),
encompassing over 210K objects and over 400K triplets. To realize SGG in
large-size SAI, we propose a context-aware cascade cognition (CAC) framework to
understand SAI regarding object detection (OBD), pair pruning and relationship
prediction for SGG. We also release a SAI-oriented SGG toolkit with about 30
OBD and 10 SGG methods which need further adaptation by our devised modules on
our challenging STAR dataset. The dataset and toolkit are available at:
https://linlin-dev.github.io/project/STAR.
