RingMo-Galaxy: A Remote Sensing Distributed Foundation Model for Diverse Downstream Tasks

Remote sensing lightweight foundation models have successfully achieved online perception, providing real-time intelligent interpretation. However, their capabilities are restricted to inferences solely based on their respective observations and models, thus lacking a comprehensive understanding of large-scale remote sensing scenarios. To address this limitation, we propose RingMo-Galaxy, a remote sensing distributed foundation model based on generalized information mapping and interaction. RingMo-Galaxy can realize online collaborative perception across multiple platforms and diverse downstream tasks by mapping observations into a unified space and implementing a task-agnostic information interaction strategy. Specifically, we leverage the ground-based geometric prior of remote sensing oblique observations to change feature mapping from absolute to relative depth estimation, thereby enhancing the model's ability to extract generalized features across diverse heights and perspectives. In addition, we present a dual-branch information compression module to decouple high-frequency and low-frequency features, achieving feature-level compression while preserving critical task-agnostic details. To support our research, we collect a multitask simulation dataset named AirCo-MultiTasks, specifically designed for multi-unmanned aerial vehicle (UAV) collaborative observation. We also conduct extensive experiments, including 3-D object detection, instance segmentation, and trajectory prediction. The numerous results demonstrate that our proposed RingMo-Galaxy achieves state-of-the-art performance across various downstream tasks.