OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data

2025-08-18

AI Summary

Earth observation satellites suffer from high data downlink and analysis latency (hours to days) due to narrow ground-to-satellite link bandwidth and short communication windows, hindering time-critical applications such as disaster response. To address this, we propose a multi-satellite collaborative on-orbit real-time analytics framework. Our approach introduces a pipelined inter-satellite cooperative computing architecture enabling Tip-and-Cue cross-constellation task orchestration, integrated with microservice-based task decomposition, dynamic traffic routing optimization, and a hardware-in-the-loop on-board computing platform—achieving end-to-end low-latency processing. Experimental evaluation demonstrates that, compared to state-of-the-art approaches, our system achieves up to a 60% improvement in analytical throughput and reduces satellite-to-ground communication overhead by 72%, thereby significantly enhancing the timeliness of geospatial situational awareness and response.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Security and Privacy: Data transparency and provenance

Abstract

Earth observation analytics have the potential to serve many time-sensitive applications. However, due to limited bandwidth and duration of ground-satellite connections, it takes hours or even days to download and analyze data from existing Earth observation satellites, making real-time demands like timely disaster response impossible. Toward real-time analytics, we introduce OrbitChain, a collaborative analytics framework that orchestrates computational resources across multiple satellites in an Earth observation constellation. OrbitChain decomposes analytics applications into microservices and allocates computational resources for time-constrained analysis. A traffic routing algorithm is devised to minimize the inter-satellite communication overhead. OrbitChain adopts a pipeline workflow that completes Earth observation tasks in real-time, facilitates time-sensitive applications and inter-constellation collaborations such as tip-and-cue. To evaluate OrbitChain, we implement a hardware-in-the-loop orbital computing testbed. Experiments show that our system can complete up to 60% analytics workload than existing Earth observation analytics framework while reducing the communication overhead by up to 72%.

Problem

• Enabling real-time Earth observation analytics for time-sensitive applications
• Overcoming limited bandwidth and delayed data download from satellites
• Orchestrating computational resources across satellite constellations efficiently

Innovation

• Collaborative analytics framework across satellite constellations
• Microservices decomposition with optimized resource allocation
• Pipeline workflow minimizing inter-satellite communication overhead