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

• URL: http://arxiv.org/abs/2603.19858v1
• Date: Fri, 20 Mar 2026 11:20:03 GMT
• Title: Beyond detection: cooperative multi-agent reasoning for rapid onboard EO crisis response
• Abstract summary: Rapid identification of hazardous events is essential for next-generation Earth Observation missions.<n>Current monitoring pipelines are largely ground-centric, introducing latency due to downlink limitations, multi-source data fusion constraints, and the computational cost of exhaustive scene analysis.<n>This work proposes a hierarchical multi-agent architecture for onboard EO processing under strict resource and bandwidth constraints.
• Score: 39.146761527401424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks [71.01350912967922]
  In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.<n>This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services.<n>We develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-08-02T10:05:50Z)
• Agentic AI for Remote Sensing: Technical Challenges and Research Directions [37.28125428920207]
  Earth Observation (EO) is moving beyond static prediction toward multi-step analytical that require coordinated reasoning over data,referenced tools, and geospatial state.<n>This paper argues that these challenges are structural assumptions rather than incidental.<n>We then outline design principles for EO-native agents centered on structured geospatial state, tool-aware reasoning, verifier-guided execution, and learning objectives aligned with geospatial and physical validity.
  arXiv  Detail & Related papers  (2026-04-27T18:59:49Z)
• OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents [68.85365034738534]
  We introduce a unified framework for developing tool-augmented geospatial agents trained on satellite imagery, natural-language queries, and detailed reasoning traces.<n>The training pipeline relies on supervised fine-tuning over structured reasoning trajectories, aligning the model with verified multistep tool interactions.<n>The accompanying corpus comprises 14,538 training and 1,169 evaluation instances, with more than 100K reasoning steps in the training split and over 7K reasoning steps in the evaluation split.
  arXiv  Detail & Related papers  (2026-02-19T18:59:54Z)
• Agentic Spatio-Temporal Grounding via Collaborative Reasoning [80.83158605034465]
  Temporal Video Grounding aims to retrieve thetemporal tube of a target object or person in a video given a text query.<n>We propose the Agentic Spatio-Temporal Grounder (ASTG) framework for the task of STVG towards an open-world and training-free scenario.<n>Specifically, two specialized agents SRA (Spatial Reasoning Agent) and TRA (Temporal Reasoning Agent) constructed leveraging on modern Multimoal Large Language Models (MLLMs)<n>Experiments on popular benchmarks demonstrate the superiority of the proposed approach where it outperforms existing weakly-supervised and zero-shot approaches by a margin
  arXiv  Detail & Related papers  (2026-02-10T10:16:27Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classification [0.0]
  This work introduces a cascading multi-agent framework that unifies complementary paradigms into a coherent and interpretable architecture.<n>Early modules perform reconstruction-gated filtering and object-level assessment, while higher-level reasoning agents are selectively invoked to interpret semantically ambiguous events.<n>The framework advances beyond conventional detection pipelines by combining early-exit efficiency, adaptive multi-agent reasoning, and explainable anomaly attribution, establishing a reproducible and energy-efficient foundation for scalable intelligent visual monitoring.
  arXiv  Detail & Related papers  (2026-01-08T11:31:47Z)
• Agentic AI Framework for Cloudburst Prediction and Coordinated Response [0.8697317909540486]
  The paper outlines an agentic artificial intelligence system to study atmospheric water-cycle intelligence.<n>The framework uses autonomous but cooperative agents that reason, sense, and act throughout the entire event lifecycle.<n>It provides a platform of scalable adaptive and learning-based climate resilience.
  arXiv  Detail & Related papers  (2025-11-27T21:33:03Z)
• An LLM-Powered AI Agent Framework for Holistic IoT Traffic Interpretation [1.3832473221638348]
  This work presents an LLM-powered AI agent framework that converts raw packet captures into structured and semantically enriched representations for interactive analysis.<n>An AI agent guided by a large language model performs reasoning over the indexed traffic artifacts, assembling evidence to produce accurate and human-readable interpretations.
  arXiv  Detail & Related papers  (2025-10-15T12:30:01Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.