Remote sensing lightweight foundation models have achieved notable success in
online perception within remote sensing. However, their capabilities are
restricted to performing online inference solely based on their own
observations and models, thus lacking a comprehensive understanding of
large-scale remote sensing scenarios. To overcome this limitation, we propose a
Remote Sensing Distributed Foundation Model (RS-DFM) based on generalized
information mapping and interaction. This model can realize online
collaborative perception across multiple platforms and various 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 transform the feature
mapping from absolute depth estimation to relative depth estimation, thereby
enhancing the model's ability to extract generalized features across diverse
heights and perspectives. Additionally, we present a dual-branch information
compression module to decouple high-frequency and low-frequency feature
information, achieving feature-level compression while preserving essential
task-agnostic details. In support of our research, we create a multi-task
simulation dataset named AirCo-MultiTasks for multi-UAV collaborative
observation. We also conduct extensive experiments, including 3D object
detection, instance segmentation, and trajectory prediction. The numerous
results demonstrate that our RS-DFM achieves state-of-the-art performance
across various downstream tasks.

提出了一种基于广义信息映射和交互的远程感知分布式基础模型（RS-DFM），可以通过将观测映射到统一空间并实施任务不可知的信息交互策略，实现多平台和各种下游任务的在线协同感知。