AeroVerse-SatAgent: UAV-Satellite Collaborative Spatial Reasoning Inspired by the Dual Visual Pathway Theory of Cognitive Neuroscience

2026-06-30

AI Summary

Existing drone-based spatial reasoning methods suffer from inconsistent performance under viewpoint and scale variations due to single-view occlusions, perspective distortions, and the lack of explicit geometric modeling in vision-language models. Inspired by the dual-stream mechanism of human visual processing, this work proposes the first aerial-ground collaborative spatial reasoning framework that integrates multi-view information from unmanned aerial vehicles (UAVs) and satellites. The authors introduce SatAgent-SR130K, a large-scale UAV-satellite dataset, and develop a geometry-aware 3D reconstruction encoder, a multi-view topological-semantic alignment module, and a unified bird’s-eye-view (BEV) coordinate system, complemented by a multi-view consistency loss. The proposed approach outperforms both general-purpose foundation models and specialized spatial reasoning models by up to 25.91% and 11.69%, respectively, significantly enhancing geometric relational reasoning in complex urban environments.

Abstract

With the rapid advancement of aerospace embodied intelligence, enabling Unmanned Aerial Vehicles (UAVs) to autonomously understand and reason about complex environments has become increasingly important. However, existing UAV-based spatial reasoning approaches face critical limitations: single-view perception renders them vulnerable to occlusions and perspective distortions, while most VLMs lack explicit geometric modeling, relying on semantic cues and yielding inconsistent reasoning under viewpoint and scale variations. To address these challenges, we propose SatAgent, a UAV-Satellite collaborative spatial reasoning model inspired by the dual-pathway mechanism of the human visual system. By jointly leveraging satellite and UAV perspectives, SatAgent enables robust, accurate reasoning in complex urban environments. We first introduce a Geometric-Aware 3D Reconstruction Encoder that elevates 2D UAV features into explicit 3D spatial representations. Next, we design a multi-view topology-semantic alignment module integrating cross-view features within a unified BEV coordinate system. We further introduce a multi-view consistency loss encouraging viewpoint-invariant representations. Finally, we construct SatAgent-SR130K, the first large-scale UAV-Satellite collaborative multi-view spatial reasoning dataset. Experiments show SatAgent outperforms state-of-the-art general-purpose foundation models and specialized spatial reasoning models by 25.91\% and 11.69\%, respectively, across diverse tasks, achieving particularly high accuracy in complex geometric relationship reasoning.

Problem

• UAV
• spatial reasoning
• occlusion
• geometric modeling
• viewpoint variation

Innovation

• UAV-Satellite Collaboration
• Dual Visual Pathway
• Geometric-Aware 3D Reconstruction
• Multi-view Consistency
• BEV Spatial Reasoning