mlsanityData to 14 Oct 2026

SAT-Edge-Agent: Hardware-in-the-Loop Edge-Agent Orchestration for Onboard Satellite Intelligence

arXiv 2608.03728 ↗First posted 4 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

Onboard satellite intelligence requires a task layer that translates mission intent into local tool calls, exposes execution state, and returns machine-consumable artifacts under communication and power constraints. We present SAT-Edge-Agent, a hardware-in-the-loop (HIL) edge-agent system deployed on a commercial off-the-shelf ARM-based heterogeneous edge system-on-chip. A browser workspace and FastAPI agent coordinate a local OpenAI-compatible language service with a project-internal YOLO-style oriented-object-detection endpoint that returns FAIR1M metadata-backed structured results. Two fixed FAIR1M workloads, one single-image and one serial two-image request, were repeated 20 times each and completed 20/20 attempts. Mean Full-Agent latency was 29.353 s and 60.937 s, with empirical P95 values of 31.166 s and 66.882 s. Mean detector time was 861.386 ms and 1510.920 ms, only 2.93% and 2.48% of the corresponding Full-Agent means. Profiling indicates that most visible latency occurs outside detector execution. Mean CPU utilization was 20.761% and 20.482%. A 200-ms NPU-load field averaged 100% for both workloads, but it represents a shared-accelerator software field rather than detector-only occupancy or calibrated utilization. The public evidence package provides sanitized request-level records, redacted JSON, normalized SSE examples, and scripts reproducing the reported statistics. These results establish a reproducible HIL boundary for observable satellite edge-agent orchestration, but do not establish detector accuracy, a new geolocation method, calibrated energy efficiency, or flight readiness.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 4 Aug 2026.

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References · 10

  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  2. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  3. 2017DOTA: A Large-scale Dataset for Object Detection in Aerial Imagescited by 623
  4. 2021FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagerycited by 143
  5. 2022Satellite edge computing for real-time and very-high resolution Earth observationcited by 12
  6. 2023Fast model inference and training on-board of Satellitescited by 11
  7. 2024Benchmarking Edge AI Platforms for High-Performance ML Inferencecited by 9
  8. 2023Oriented object detection in optical remote sensing images using deep learning: a surveycited by 8
  9. 2025Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellitescited by 7
  10. 2026Beyond detection: cooperative multi-agent reasoning for rapid onboard EO crisis responsecited by 1

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