DynaFilter: Cloud-driven Dynamic Filtering for Satellite Edge Intelligence

• URL: http://arxiv.org/abs/2607.10098v1
• Date: Sat, 11 Jul 2026 03:28:25 GMT
• Title: DynaFilter: Cloud-driven Dynamic Filtering for Satellite Edge Intelligence
• Abstract summary: DynaFilter is a dynamic filtering technique that enables satellite edge devices to perform selective region-of-interest (RoI) inference directly in the compressed-domain.<n>We show that DynaFilter reduces the total volume of pixel data for decoding and subsequent inference by 1.6x-7.1x for images.<n>It achieves 92.0% bandwidth savings for video streams compared to state-of-the-art baselines.
• Score: 24.492347669238175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern satellite edge systems, including those performing remote sensing tasks such object detection and tracking, are characterized by severely limited bandwidth and intermittent connections, making continuous data transmission to the cloud impractical. Existing edge-cloud systems, however, either require heavy pre-processing before analysis, for instance, full decompression of imagery data, or transmit all compressed data regardless of relevance. To address these challenges, we design DynaFilter, a dynamic filtering technique that enables satellite edge devices to perform selective region-of-interest (RoI) inference directly in the compressed-domain, without full decompression. Our key insight is that low-level compression syntax, specifically DC coefficients/AC energy in JPEG images and motion vectors in video streams, exhibits strong correlations with high-level semantic queries. By establishing a precise mapping between cloud query semantics and multimodal compressed-domain features, DynaFilter enables the edge to identify and transmit only relevant data associated to RoIs. Extensive evaluations show that DynaFilter reduces the total volume of pixel data for decoding and subsequent inference by 1.6x-7.1x for images, and achieves 92.0% bandwidth savings for video streams compared to state-of-the-art baselines. Furthermore, it decreases energy consumption by 43.1-88.6% on target devices and achieves a 1.6x-3.0x speedup in inference latency.

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