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

arXiv: https://arxiv.org/abs/2508.13374 · HTML: https://mlsanity.com/paper/2508.13374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [A Survey on Non-Geostationary Satellite Systems: The Communication Perspective](https://mlsanity.com/paper/2107.05312/) (2021)
- [Satellite edge computing for real-time and very-high resolution Earth observation](https://mlsanity.com/paper/2212.12912/) (2022)
- [Understanding the limits of LoRaWAN](https://mlsanity.com/paper/1607.08011/) (2016)
- [Earth+: on-board satellite imagery compression leveraging historical earth observations](https://mlsanity.com/paper/2403.11434/) (2024)
- [Infiltrating the Sky: Data Delay and Overflow Attacks in Earth Observation Constellations](https://mlsanity.com/paper/2409.00897/) (2024)
- [Low-latency Imaging and Inference from LoRa-enabled CubeSats](https://mlsanity.com/paper/2206.10703/) (2022)
- [Deciphering the Enigma of Satellite Computing with COTS Devices: Measurement and Analysis](https://mlsanity.com/paper/2401.03435/) (2024)
- [B2LoRa: Boosting LoRa Transmission for Satellite-IoT Systems with Blind Coherent Combining](https://mlsanity.com/paper/2505.24140/) (2025)

## Abstract (arXiv)

Earth observation analytics have the potential to transform many sectors. However, due to limited ground connections, it currently takes hours to days to download and analyze Earth observation data, diminishing the value of data for time-sensitive applications like disaster monitoring or search-and-rescue. To enable real-time analytics, we propose OrbitChain, an in-orbit multi-satellite Earth analytics framework. OrbitChain uses a pipelined design to decompose workflows into analytics functions, and orchestrates constellation-wide resources to finish real-time analytics tasks. It provides timely insights to Earth sensing applications and enables advanced workflows like in-orbit tip-and-cue. Hardware-in-the-loop experiments show that OrbitChain can deliver analytics results in minutes, supports up to 60% more analytics workload than existing frameworks, and reduces inter-satellite communication overhead by up to 45%.