arXiv:2606. 00445v1 Announce Type: cross Abstract: Dark vessel detection requires fusing what vessels report through AIS with what satellites observe through radar and optical sensors.
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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.
arXiv:2609.37003v1 Announce Type: new Abstract: Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most e...
Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most existing datasets predominantly focus on general o...
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.
arXiv:2601. 11665v3 Announce Type: replace Abstract: Unmanned Aerial Vehicles (UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) domain.
The paper presents a two‑stage cascade for near‑real‑time detection of forest anomalies using Sentinel‑1 SAR time series. The first stage applies an adaptive robust‑statistics z‑score test on co‑registered VH backscatter against a same‑season historical baseline, while the second stage uses a convolutional autoencoder’s structural similarity metric to confirm disturbances. Alerts are issued only when both stages agree, with confidence scores and risk tiers based on repeat‑occurrence history, and the system outputs area estimates compatible with MRV workflows.