EarthSight: A Distributed Framework for Low-Latency Satellite Intelligence
Ansel Kaplan Erol, Seungjun Lee, and Divya Mahajan

TL;DR
EarthSight is a distributed framework that enhances low-latency satellite image analysis by optimizing onboard and ground-based processing, reducing latency and resource usage for critical applications.
Contribution
It introduces multi-task inference, a ground-station scheduler, and dynamic filtering to improve scalability and efficiency in satellite image intelligence.
Findings
Reduces average compute time per image by 1.9x.
Lowers 90th percentile latency from 51 to 21 minutes.
Enables scalable, low-latency analysis within bandwidth and power constraints.
Abstract
Low-latency delivery of satellite imagery is essential for time-critical applications such as disaster response, intelligence, and infrastructure monitoring. However, traditional pipelines rely on downlinking all captured images before analysis, introducing delays of hours to days due to restricted communication bandwidth. To address these bottlenecks, emerging systems perform onboard machine learning to prioritize which images to transmit. However, these solutions typically treat each satellite as an isolated compute node, limiting scalability and efficiency. Redundant inference across satellites and tasks further strains onboard power and compute costs, constraining mission scope and responsiveness. We present EarthSight, a distributed runtime framework that redefines satellite image intelligence as a distributed decision problem between orbit and ground. EarthSight introduces three…
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