LEO Satellite Constellation for Global-Scale Remote Sensing With On-Orbit Cloud AI Computing
Yuejin Li, Mi Wang, Kai Hwang, Zhengdao Li, Tongkai Ji
Abstract
This paper proposes a new satellite-based framework for global-scale remote sensing that is integrated with on-orbit cloud computing and AI ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">artificial intelligence</i> ) services. These spaced-based services cover the entire earth surfaces using massive LEO satellite constellation. Global-scale sensing of earth resources must be supported by massive number of LEO satellites equipped with cloud/AI computing services in real time. New satellite computer architectural features are presented along with some satellite constellation deployment topologies. We design satellite-based computers to support on-orbit remote sensing and AI scene analysis. This demands real-time performance without transmitting the sensed data back to earth for delayed processing. Notable space data services include on-orbit data sensing of large areas, machine learning from earth resources data, earth scene/event analysis, geomorphology observation, smart city management, disaster relief, global healthcare IoT, and environmental ecology protection, etc. We attempt to achieve high-efficiency earth resources utilization along with green energy, low cost, and robustness in real-life services.