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。

RS-DFM：一种用于多样化下游任务的遥感分布式基础模型

RS-DFM: A Remote Sensing Distributed Foundation Model for Diverse  Downstream Tasks

The problem of determining if a military unit has correctly understood an
order and is properly executing on it is one that has bedeviled military
planners throughout history. The advent of advanced language models such as
OpenAI's GPT-series offers new possibilities for addressing this problem. This
paper presents a mechanism to harness the narrative output of large language
models and produce diagrams or "maps" of the relationships that are latent in
the weights of such models as the GPT-3. The resulting "Neural Narrative Maps"
(NNMs), are intended to provide insight into the organization of information,
opinion, and belief in the model, which in turn provide means to understand
intent and response in the context of physical distance. This paper discusses
the problem of mapping information spaces in general, and then presents a
concrete implementation of this concept in the context of OpenAI's GPT-3
language model for determining if a subordinate is following a commander's
intent in a high-risk situation. The subordinate's locations within the NNM
allow a novel capability to evaluate the intent of the subordinate with respect
to the commander. We show that is is possible not only to determine if they are
nearby in narrative space, but also how they are oriented, and what
"trajectory" they are on. Our results show that our method is able to produce
high-quality maps, and demonstrate new ways of evaluating intent more
generally.

本文介绍了一种基于 OpenAI GPT-3 语言模型的机制，利用大型语言模型的叙述输出生成图表或 “地图”，旨在提供对模型中信息，观点和信仰组织方式的洞察，进而提供理解物理距离下意图和响应的手段。研究问题以及具体实现方法的讨论涉及了信息空间映射，命令意图的推断和下级意图评估，并证明了所提出方法的有效性。