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
arXiv:2610.29313v1 Announce Type: new
Abstract: Multimodal ocean modeling remains challenging due to substantial heterogeneity in data structures, physical semantics, and spatiotemporal characteristi...
By Sen Jia, Siqi Wei, Junyu Wang, Zijun Lv, Pan Liu, Meng Xu, Xiyou Fu, Yuxi Sun, Qiang Liu, Shuyu Zhang, Lin Mu
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.
By Thomas Goudemant, Clotilde Szywala, Benjamin Francesconi, Michelle Aubrun, Yves Bobichon, Marjorie Bellizzi, Adrien Girard
arXiv:2609.13206v1 Announce Type: cross
Abstract: Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such...
By Makayla McDevitt, Maike Sonnewald, Stephanie Dutkiewicz
arXiv:2509. 04682v2 Announce Type: replace-cross Abstract: Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant failure modes, gaps largely unaddressed in current UPAM practice.
By Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang, KC Santosh
arXiv:2607. 07758v1 Announce Type: new Abstract: Foundation models (FMs) have transformed machine learning from isolated task-specific model development toward general-purpose models pretrained on broad data and adapted to multiple downstream tasks.
By Syed Usama Imtiaz, Mitra Nasr Azadani, Nasrin Alamdari
OceanDepths is the first open, global, AI‑ready dataset that pairs satellite‑derived sea surface temperature, salinity, and height with co‑located EN4 subsurface temperature and salinity profiles, complemented by GLORYS12 reanalysis data. It covers 2000–2024 at 0.1°×0.1° spatial resolution and weekly temporal resolution, providing over 9.5 million paired profiles interpolated to 50 depth levels. The dataset’s 4‑D multivariate structure, high resolution, long temporal extent, and extreme sparsity of subsurface observations make it a challenging testbed for novel AI methods, with demonstrated use in subsurface state reconstruction and potential for observation‑based forecasting.
By Simon Donike, Ruben Cartuyvels, Antonino Ian Ferola, Elisa Carli, Diego Fernandez Prieto, Marie-Helene Rio
arXiv:2607. 05207v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task.
By Rohita Mocharla, Vishal M. Patel
arXiv:2607. 22205v2 Announce Type: replace-cross Abstract: Remote sensing multimodal large language models (RS-MLLMs) have improved general aerial-image understanding.
By Yuheng Zong, Minghua Wang, Xin Zhao, Zhi-Hui Zhan, Antonio Plaza, Jon Atli Benediktsson
arXiv:2606. 23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.
By Muallim Yakubu, Vwavware Oruaode Jude
arXiv:2604. 26051v2 Announce Type: replace-cross Abstract: The increasing number of satellites has improved the temporal resolution of Earth observation, making satellite-based flood mapping a promising approach for operational flood monitoring.
By Hyunho Lee, Wenwen Li
arXiv:2610.33609v1 Announce Type: cross
Abstract: Satellite-derived bathymetry (SDB) is often proposed as a low-cost complement to multibeam surveys of ports, but its accuracy is seldom measured on a...
By Muhammad Saeed, Fatema Al Hajri, Mohamed H. Alhosani, Dmitry Mikhaylov, Yasser F. Al Wahedi