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...
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...
By Danfeng Hong, Chenyu Li, Jocelyn Chanussot
arXiv:2608. 09360v1 Announce Type: cross Abstract: The demand for maritime surveillance has given rise to the need for monitoring fishing vessel activities, particularly in addressing the challenge of "dark vessels" that operate without Automatic Identification System (AIS) transmission.
By Shantakar Mohanty, Prasun Kumar Gupta, Raian Vargas Maretto
The paper compares classical machine learning algorithms—such as Logistic Regression, SVM, Random Forest, XGBoost, and CatBoost—with Tabular Deep Learning models (TabNet, FT-Transformer, TabTransformer, TabSeq, and 1D CNNs) for urban land cover classification using a UCI dataset derived from high‑resolution aerial imagery. It evaluates performance across nine land cover classes, addressing challenges like high dimensionality, heterogeneous features, and class imbalance by applying weighted cross‑entropy loss for deep models and measuring accuracy, macro‑precision, macro‑recall, macro‑F1, AUC‑ROC, and confusion matrices. Results indicate that while tree ensembles remain strong baselines, Tabular Deep Learning can match or surpass them when non‑linear interactions are prominent and imbalance handling is effective.
By Muntasir Tabasum, Tanpia Tasnim, Md. Ekramul Islam, Al Zadid Sultan Bin Habib
KSG‑Net introduces a Key‑Sparse and Global‑Context learning framework for maritime 3D ship detection, addressing weak feature representation of small, sparse vessels and limited global modeling of large vessels. The network employs a Key Sparse Multi‑scale Aggregation module to select informative voxels and aggregate cross‑scale features, and a Global Context Aggregation module to capture long‑range geometric dependencies via scene‑level context modeling. Experiments on the Thames River vessel dataset and simulated data show that KSG‑Net outperforms existing methods in multi‑scale vessel detection and remains robust in complex maritime environments.
By Zhouyuan Huai, Meiqi Wan, Yan Yang, Minshi Chen, Xin Yuan, Wei Wang, Xiao Wang
arXiv:2609.16347v1 Announce Type: new
Abstract: Sea-ice type prediction is important for climate monitoring, maritime navigation, and decision-making in polar regions. The main source of label data f...
By Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett, Farnoush Banaei-Kashani