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arXiv AI

On-Board Anomaly Detection for Efficient Marine Environmental Monitoring

The paper presents a marine event detection pipeline for Earth observation satellites that uses a self‑supervised neural network encoder to compress images into a latent space, followed by a machine learning anomaly detection model to spot deviations from normal sea patterns. It compares this approach with traditional algorithms such as Isolation Forest, One‑Class Support Vector Machine, and Local Outlier Factors, demonstrating superior performance. The lightweight, resource‑efficient design is tailored for satellites with limited computational resources and has been integrated into missions like ESA’s Phisat‑2 and Microsoft/Thales Alenia Space IMAGIN‑e, enabling timely alerts and efficient data reduction.

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arXiv AI
Oct 9

Onboard Marine Anomaly Detection on $\Phi$sat-2: From Simulation-Based Development to In-Orbit Demonstration

The paper reports on the development, integration, and in-orbit testing of a lightweight marine anomaly detection pipeline for ESA’s Φsat‑2 mission. The system combines sea segmentation, self‑supervised feature encoding, generic anomaly detection, and optional anomaly characterization, and was initially trained on simulated imagery before being retrained on real Level‑1 data to address a mismatch between simulation and in‑orbit observations. The experience underscores the value of simulation for pre‑flight risk reduction, the necessity of robust annotation strategies, and the importance of sensor‑aware design for reliable scientific validation.

By Clotilde Szywala, Thomas Goudemant, Marjorie Bellizzi, Benjamin Francesconi, Adrien Girard
computer-vision
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arXiv Computer Vision
Sep 14

AquaCubeAI-Powered Monitoring Turbidity on-board {\Phi}sat-2

arXiv:2609.12744v1 Announce Type: new Abstract: Timely monitoring of coastal water quality is critical for environmental protection, yet conventional satellite workflows rely on downlink and ground p...

By Pietro Di Stasio, Francesca Razzano, Elisa Liparulo, Gabriele Meoni, Nicolas Long\'ep\'e, Deodato Tapete, Paolo Gamba, Gilda Schirinzi, Silvia Liberata Ullo
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arXiv AI
Jun 26

On-board Remote-Sensing Foundation Models for Unsupervised Change Detection of Disaster Events

arXiv:2606. 27018v1 Announce Type: cross Abstract: Remote Sensing Foundation Models (RSFMs) have emerged as a powerful alternative to supervised models for Earth Observation, allowing satellites to autonomously trigger high-resolution captures or adjust tasking parameters upon detecting an anomaly, thereby maximizing the utility of the mission's limited power and computational resources.

By S. Ram\'irez-Gallego
diffusion
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arXiv Machine Learning
Jul 31

ARES: Anomaly Recognition Model For Edge Streams

arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.

By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer
ragdiffusion
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arXiv AI
Oct 9

AI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware

The paper presents an AI-based system for onboard maritime object detection on Earth‑observation satellites, aiming to reduce data volume by selectively downlinking relevant vessel information. A YOLOX‑S detector is trained on a dataset of 68 Maxar scenes with 43 vessel classes, then quantized and deployed on a Versal VC1902 DPU with minimal loss in accuracy and a few‑second processing time per scene. Various downlink modes—metadata only, image crops, tiles, or full scenes—are evaluated, showing that tiles retain 98% of vessels while reducing scene volume to 29%, and crops retain 83% with only 3% of the volume.

By Thomas Goudemant, Aur\'elien Bobey, Omar Hlimi, Marjorie Bellizzi
computer-visionefficiency
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Hugging Face Trending Papers
Aug 3

CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection

While deep learning models have achieved state-of-the-art performance in time series anomaly detection, their complex architectures incur substantial inference overhead. Existing methods typically apply a uniform inference strategy across all data points, which is inefficient given that anomalies are inherently scarce and the vast majority of temporal data consists of predictable normal patterns.

benchmarks
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