Achieving Efficient Storage and Communication via Collaboration

Summarize First, Download Later: Onboard VLMs for Bandwidth-Efficient Earth Observation

This work proposes a "Summarize First, Download Later" paradigm that exploits recent advances in onboard edge computing and Vision-Language Models (VLMs) and substantially reduces bandwidth consumption while accelerating time-to-insight for time-sensitive missions.

Reproducible Baselines and Execution Verification for Earth Observation Satellite Constellation Scheduling

The Earth Observation Satellite Scheduling Problem (EOSSP) has seen significant algorithmic advances, yet the lack of standardized, open-source benchmarks has hindered fair comparison across studies. Recent large-scale benchmark suites have begun addressing this gap by providing high-fidelity simulation environments, but these frameworks typically focus on evaluating sophisticated methods, with limited systematic characterization of simple, reproducible baselines. This paper presents an open-source, modular scheduling framework that (1) provides complete implementations of FIFO (First-In-First-Out) and priority-based greedy heuristics as reproducible baselines, (2) offers a lightweight, extensible platform for rapid prototyping of scheduling algorithms, and (3) systematically characterizes the performance of these baselines across 50 synthetically generated test instances spanning five scales from 10 to 1,000 tasks. We formalize the scheduling problem incorporating rest-to-rest attitude manoeuvre dynamics, including angular velocity and acceleration limits, along with energy and storage constraints. Running the framework's own reference implementation surfaced two concrete defects worth reporting in their own right: a discontinuity in the originally specified slew-time kinematic model at the boundary between the triangular and trapezoidal velocity profiles, and a single-satellite scheduling gap that left additional constellation satellites unused in multi-satellite scenarios. Both are corrected here, and the corrected framework, including problem instance generators, evaluation metrics, and the two fixes, is released as open-source software. On the instances tested, priority-based greedy scheduling improves total scheduled priority over FIFO by 65 to 151 percent depending on scale, while achieving only 51.1 percent of the provably optimal objective value on small instances. This gap is reported transparently not to diminish the heuristic’s practical utility, but to quantify the headroom available for more sophisticated methods relative to the simplest viable baseline.

Clear-Weighted Bit Allocation for Satellite Downlinks

Earth-observation satellites capture more imagery than intermittent ground contacts can transmit. Onboard systems threshold a cloud detector, discard frames or tiles, and compress the survivors with a fixed codec. On expert-labeled imagery, these rules remove more than one-fifth of clear pixels, primarily through detector false positives. We train a neural codec with a clear-probability-weighted reconstruction loss, reallocating coded bytes from clouds to clear ground without requiring or transmitting a cloud map onboard. Each capture is encoded into a resumable base layer and a dependent refinement layer, while clear content is estimated from features produced by the encoder. At each contact, we causally rank arrived layers using estimated clear content, unfinished bytes, deadline slack, and aggregate deadline pressure. The scheduler serves base and computational deadlines, bounds stored residual bytes, and resumes interrupted packets. We evaluate the onboard-to-downlink pipeline using real entropy-coded bytes, orbit-derived interruptible contact capacities, and measured service time and energy on resource-constrained embedded accelerators. Clear-weighted codecs require up to 47.8\% fewer bytes than learned-compression baselines at matched clear-region quality. The optimized encoder consumes less time and energy than one pass of the cloud detector used by the frame-discard rules. Relative to fixed two-stage service on the same streams, our scheduler more than doubles deadline-full clear-content delivery for the interrupted combined cohort, reaches 83.6\% of a certified clairvoyant upper bound, and exceeds replayed reference orders in deadline-usable delivery.

Revisiting-Aware In-Orbit Edge Computing for Earth Observation

Typically, Earth observation satellites follow a rule of revisiting cycle to periodically pass over the same area of the Earth at regular intervals, which is jointly determined by their orbital properties (e.g., eccentricity, inclination) and instrument characteristics (e.g., off-nadir pointing and swath capabilities). However, we have observed delays in perceived revisiting cycles where limited satellite downlink bandwidth allows only partial images to be delivered, pushing back the timeliness of the full set of data, which we term as revisiting cycle delay. In this paper, we present a revisiting-aware in-orbit edge computing framework for Earth observation termed Stride. Stride leverages the unique orbital revisiting properties to afford historical reference revisiting images onboard, and exploits the inherent temporal redundancy in the revisiting imagery to transmit only the Regions of Interest (RoIs). Specifically, Stride comprises a mono- and multi-temporal cloud indicator to alleviate cloud contamination, a coarse-to-fine reference selector for orbit deviation correction, and an ensemble-local change detector to address inter-band complexities and pixel-level perturbations. Experiments on a Flat-Sat testbed and a constellation simulator demonstrate Stride improves the Revisiting Imagery Delivery (RID) score by up to 4.55X, decreases the connectivity latency by 5.02X, and enlarges the mapping coverage by 2.56X, yielding state-of-the-art performance.

Earth Embedding Products for Geospatial Analysis: Foundations, Applications, and Open Challenges

SkyShare: Constellation-wide Sky Sharing for LEO-Radio Astronomy Coexistence

Rapidly growing low-Earth-orbit (LEO) constellations increasingly operate in the spectrum shared with radio astronomy services (RAS), creating escalating interference risks for sensitive scientific observations. Existing mitigation mechanisms rely on reactive beam steering, or avoidance near observatories but fail to account for aggregate sidelobe emissions-leading to residual interference and substantial, unnecessary capacity loss. We present SkyShare, a constellation-wide sky-sharing system that enables predictive, interference-aware spot beam scheduling to protect radio astronomy while preserving network coverage. SkyShare integrates high-fidelity orbital prediction with International Telecommunication Union (ITU)-compliant Equivalent Power Flux Density (EPFD) modeling, and real-time observatory data via Operational Data Sharing (ODS) to jointly optimize beam-cell assignments over observation windows. To make constellation-scale coordination tractable, we introduce a concept of EPFD-budgeted Region-of-Interest(RoI) that bounds residual sidelobe interference while confining optimization to a minimal, provably sufficient set of cells. Building on RoI, we formulate LEO-RAS coexistence as a scalable scheduling problem and design SkySched, a flow-based algorithm that is optimal in special cases and yields scalable near-optimal solutions in the general NP-hard setting. SkyShare operates entirely in the control plane and requires no satellite hardware changes. Using real Starlink constellation geometries, we evaluate SkyShare across 25 single-dish, Ku-band RAS sites worldwide. Compared to Starlink boresight avoidance, SkyShare reduces unserved cells by up to 90.68% while remaining within EPFD limits.