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    This study introduces STAR, a large-scale dataset for scene graph generation (SGG) in satellite imagery (SAI). It also presents a context-aware cascade cognition (CAC) framework to address challenges in understanding complex geospatial scenarios from satellite data.

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    Area of Science:

    • Computer Vision
    • Geospatial Artificial Intelligence
    • Remote Sensing

    Background:

    • Scene graph generation (SGG) is crucial for understanding geospatial scenarios in satellite imagery (SAI).
    • Existing SGG models struggle with large-size, very-high-resolution (VHR) SAI due to scale variations and complex object relationships.
    • A significant gap exists in large-scale SGG datasets for VHR SAI.

    Purpose of the Study:

    • To construct a large-scale dataset for SGG in large-size VHR SAI.
    • To propose a novel framework for SGG tailored to the complexities of SAI.
    • To provide a toolkit to facilitate research in SAI-oriented SGG.

    Main Methods:

    • Construction of the STAR (Scene graph generaTion in lArge-size satellite imageRy) dataset, featuring over 210K objects and 400K triplets from large-size VHR SAI.
    • Development of a context-aware cascade cognition (CAC) framework for object detection (OBD), pair pruning, and relationship prediction in SGG.
    • Creation of an SAI-oriented SGG toolkit with numerous OBD and SGG methods adapted for VHR SAI.

    Main Results:

    • The STAR dataset provides a comprehensive resource for SGG in large-size VHR SAI.
    • The CAC framework demonstrates effectiveness in addressing long-range contextual reasoning for SGG in complex satellite scenes.
    • The released toolkit supports adaptation of existing methods and encourages further research.

    Conclusions:

    • The STAR dataset and CAC framework advance the field of SGG in satellite imagery.
    • Addressing the unique challenges of VHR SAI is essential for robust geospatial understanding.
    • The developed resources will accelerate progress in cognitive geospatial AI.