arXiv:2606. 26559v1 Announce Type: cross Abstract: Earth observation satellite networks generate massive volumes of high-resolution imagery, whereas inter-satellite and downlink resources remain limited.
arXiv:2607. 10098v1 Announce Type: cross 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.
The paper proposes a task-oriented semantic feature transmission framework for satellite remote sensing over low‑signal‑to‑noise ratio (SNR) channels. Instead of reconstructing images first, it directly transmits semantic features extracted by a multitask‑pretrained backbone, using a lightweight channel adaptation module to reduce bandwidth and a feature restorer to recover task‑relevant structure after channel corruption. Experiments on scene classification and object detection under additive white Gaussian noise show consistent improvements over reconstruction‑oriented joint source‑channel coding baselines, especially in the low‑SNR regime.
The paper introduces Rift, a two‑stage system that reduces the computational load of vision‑language models on satellites by pruning image tiles that do not affect the answer and then applying elastic prefill to limit token usage. By exploiting answer‑invariant token redundancy, Rift cuts energy consumption by 78 % and latency by 69 % compared to exhaustive tiled inference, while boosting accuracy from 45 % to 73 % on LLaVA‑1.5 7B running on a Jetson AGX Orin.
arXiv:2607. 24790v1 Announce Type: new Abstract: Low-Earth orbit (LEO) satellite Internet has become an important infrastructure for enabling ubiquitous connectivity to align with the International Telecommunications Union vision for 6G telecommunications networks.
arXiv:2606.27223v3 Announce Type: replace Abstract: Gaussian Splatting has been recently explored for satellite 3D reconstruction, demonstrating flexibility and efficiency in representing radiometric...
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.
Gaussian Splatting has been recently explored for satellite 3D reconstruction, demonstrating flexibility and efficiency in representing radiometrically diverse satellite scenes. However, the limited top viewpoint of satellite imagery results in insufficient supervision on building facades, leaving surface holes and degraded visual fidelity.
Genesis is a generative engine designed to synthesize complete, globally consistent quadtree pyramids for satellite imagery. It tackles the new multi‑scale tile completion task by combining a vertical super‑resolution model with a horizontal mask‑based outpainting model, enabling seamless generation across arbitrary zoom levels and positions. The authors also release dense500, a fully observed multi‑scale dataset, and a suite of pyramid‑level metrics to benchmark performance.
arXiv:2605.19892v2 Announce Type: replace-cross Abstract: Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresp...
Genesis is a generative engine that produces fully consistent multi‑scale satellite image pyramids by combining a vertical super‑resolution model with a horizontal mask‑based outpainting model. It addresses the lack of existing methods that can synthesize a complete quadtree from sparse seed tiles at arbitrary zoom levels and positions, ensuring coherence across both scale and space. The authors also release dense500, a comprehensive multi‑scale dataset and evaluation suite, to benchmark this new task.
arXiv:2606. 18271v1 Announce Type: new Abstract: As Earth Observation data generation outpaces downlink bandwidth and human-in-the-loop processing, a widening gap has emerged between onboard collection and actionable ground intelligence.
arXiv:2505.15147v3 Announce Type: replace Abstract: Remote sensing images (RSIs) capture both natural and human-induced changes on the Earth's surface. Semantic segmentation (SS) of RSIs enables the...