AI & ML
In-Orbit Computing for Remote Sensing: Task-Oriented Processing and Transmission
arXiv eess.SP0 views
arXiv:2610.30693v1 Announce Type: new Abstract: The growing volume and timeliness requirements of remote sensing data are exposing the limitations of the conventional "transmit-then-process" architecture, particularly under intermittent satellite-ground connectivity, constrained downlink bandwidth, and limited onboard computing resources. This article presents a semantic-aware space-ground collaborative remote sensing (SSGC-RS) framework, in which remote sensing data are processed to different semantic depths according to task requirements and available communication-computing resources. Three operating modes are considered, ranging from fully onboard task inference to semantic representation delivery and space-ground split inference. A land-cover recognition case study illustrates how these modes address different communication and computing bottlenecks. Finally, emerging challenges and future directions for intelligent remote sensing systems are discussed.
Source：https://arxiv.org/abs/2610.30693PreviousDamage detection in railway wheels from impact-induced ultrasonic ring-down using self-referenced decay featuresNextElectromagnetic Modeling of Deformation-Based Pinching Antennas
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