Beyond detection: cooperative multi-agent reasoning for rapid onboard EO crisis response

TL;DR

This paper introduces a hierarchical multi-agent onboard processing system for Earth Observation satellites that enhances rapid hazard detection by coordinating specialized AI agents, reducing computational load, and enabling autonomous decision-making.

Contribution

It presents a novel multi-agent architecture that integrates vision-language models and remote sensing tools for efficient onboard hazard detection under resource constraints.

Findings

Significantly reduces computational overhead in wildfire and flood detection scenarios.

Maintains coherent decision outputs despite resource limitations.

Demonstrates feasibility of distributed agent-based reasoning in orbit.

Abstract

Rapid identification of hazardous events is essential for next-generation Earth Observation (EO) missions supporting disaster response. However, current monitoring pipelines remain largely ground-centric, introducing latency due to downlink limitations, multi-source data fusion constraints, and the computational cost of exhaustive scene analysis. This work proposes a hierarchical multi-agent architecture for onboard EO processing under strict resource and bandwidth constraints. The system enables the exploitation of complementary multimodal observations by coordinating specialized AI agents within an event-driven decision pipeline. AI agents can be deployed across multiple nodes in a distributed setting, such as satellite platforms. An Early Warning agent generates fast hypotheses from onboard observations and selectively activates domain-specific analysis agents, while a Decision agent consolidates the evidence to issue a final alert. The architecture combines vision-language models, traditional remote sensing analysis tools, and role-specialized agents to enable structured reasoning over multimodal observations while minimizing unnecessary computation. A proof-of-concept implementation was executed on the engineering model of an edge-computing platform currently deployed in orbit, using representative satellite data. Experiments on wildfire and flood monitoring scenarios show that the proposed routing-based pipeline significantly reduces computational overhead while maintaining coherent decision outputs, demonstrating the feasibility of distributed agent-based reasoning for future autonomous EO constellations.