In the midst of a rapidly evolving economic landscape, the global demand for oil is steadily escalating. This increased demand has fueled marine extraction and maritime transportation of oil, resulting in a consequential and uneven surge in maritime oil spills. Characterized by their abrupt onset, rapid pollution dissemination, prolonged harm, and challenges in short-term containment, oil spill accidents pose significant economic and environmental threats. Consequently, it is imperative to adopt effective and reliable methods for timely detection of oil spills to minimize the damage inflicted by such incidents. Leveraging the YOLO deep learning network, this paper introduces a methodology for the automated detection of oil spill targets. The experimental data pre-processing incorporated denoise, grayscale modification, and contrast boost. Subsequently, realistic radar oil spill images were employed as extensive training samples in the YOLOv8 network model. The trained detection model demonstrated rapid and precise identification of valid oil spill regions. Ultimately, the oil films within the identified spill regions were extracted utilizing the simulated annealing particle swarm optimization (SA-PSO) algorithm. The proposed method for offshore oil spill survey presented here can offer immediate and valid data support for regular patrols and emergency reaction efforts.

As Arctic sea ice continues to melt and global demand for clean energy rises, Russia’s Liquefied Natural Gas (LNG) exports via the Northern Sea Route (NSR) are rapidly increasing. To ensure the operational safety of LNG carriers and safeguard the economic interests of stakeholders, including shipowners, a thorough assessment of the navigability of various ice-class LNG carriers along this route is essential. This study collected Arctic ice condition data from 2014 to 2023 and applied the Polar Operational Limit Assessment Risk Indexing System (POLARIS) methodology to calculate the Risk Index Outcome (RIO) for LNG carriers with No Ice Class, Arc4, and Arc7 ice classifications in Arctic waters. A navigability threshold of 95% RIO ≥ 0 was established to define navigable windows, and critical waters were identified where sections of the route remain in hazardous or risky conditions year-round. The results indicate that for No Ice Class vessels, Arc4 vessels, and Arc7 vessels, the navigable windows for westbound Route 1 and Route 2 under light, normal, and heavy ice conditions range from 70 to 133 days, 70 to 365 days, and 70 to 365 days, respectively, while for eastbound Route 3, the navigable windows range from 0 to 84 days, 0 to 238 days, and 7 to 365 days, respectively. The critical waters affecting the navigability of No Ice Class vessels, Arc4 vessels, and Arc7 vessels are primarily located in the Kara Sea, Laptev Sea and East Siberian Sea. This study, using the POLARIS methodology, provides valuable insights into the navigability of LNG carriers with different ice classes along the NSR, supporting the development and utilization of Arctic energy and shipping routes while offering decision-making support for stakeholders involved in Arctic maritime operations.

At present, sea ice remains a critical factor affecting the safety of vessel operations along the Northern Sea Route (NSR). However, inconsistencies between the navigability outcomes derived from the criteria for the admission of ships in the area of the Northern Sea Route (NSR criteria) and the polar operational limit assessment risk indexing system (POLARIS) methodology present challenges for navigational decision-making. This study aims to conduct a systematic comparison of the POLARIS methodology and the NSR criteria in evaluating the navigability of independently operating vessels classified as Arc4 to Arc9. Through comparative calculations of navigability and the navigability rates for six ice-class vessels across 27 districts using the two methods, this study reveals the consistencies and discrepancies in their navigability outcomes. Firstly, using the POLARIS methodology, the risk index outcome (RIO) is calculated for six ice-class vessels across 27 districts. For these districts, the navigability threshold is defined when 95% or more of the area exhibits an RIO greater than or equal to zero. Secondly, using the NSR criteria, navigability ratios for six ice-class vessels under varying ice conditions are evaluated. A navigability threshold is defined when 95% or more of the ice conditions in a district are classified as navigable. Finally, a quantitative comparison of the weekly navigability ratios obtained by the two methods is conducted to reveal the consistencies and discrepancies in the navigability outcomes of each ice-class vessel across different NSR districts. The results indicate that the consistency between the navigability outcomes of the two methods decreases with lower vessel ice classes, particularly in September and March. In general, the consistency of performance between the two methods in terms of navigability outcomes deteriorates as the vessel ice class decreases and ice conditions become more complex. This study provides a scientific foundation and data-based support for route planning and real-time decision-making in polar waters.

The Polar Operational Limit Assessment Risk Indexing System (POLARIS) has been established as a viable framework for assessing operational capabilities and associated risks in polar waters. Despite its inherent suitability for high-latitude territories, ships navigating through seasonal ice-infested waters at lower latitudes also encounter critical safety, environmental, and economic issues exacerbated by the presence of ice. This necessitates a reliable and adaptable methodology that can serve as a reference for devising effective countermeasures. This study evaluated the use of POLARIS in the intricate ice conditions prevalent in the northern navigable waters (channels and anchorages) within Liaodong Bay of the Bohai Sea, located at relatively low latitudes. Using GF-4 satellite imagery, ice conditions were collected, and the POLARIS methodology was employed to calculate Risk Index Outcome (RIO) values for non-ice-strengthened vessels during the winter season of 2021–2022. The results showed that sectors 3, 4, 5, 7, 9, 10, and 11 within the northern part of Liaodong Bay exhibited a higher risk, with sectors 5 and 10 exhibiting the most significant risk, while sectors 1 and 2 demonstrated relatively lower risk levels. The concurrence of these findings with acknowledged ice patterns and local maritime practices confirms the applicability of the POLARIS methodology in saline, seasonally ice-covered seas. Notably, the combination of POLARIS with high-resolution satellite imagery facilitated a more precise and rapid assessment of ice risk, thereby enhancing situational awareness and informing decision-making processes in maritime operations under icy conditions. In addition, this study provides preliminary evidence that POLARIS is suitable for fine-scale scenarios, in addition to being applicable to sparse-scale scenarios, such as polar waters, especially with high-resolution ice data. At the same time, this study highlights the potential of POLARIS as a disaster prevention strategy and a tool for the maritime industry to address ice challenges.

In the context of global warming, polar tourism is developing rapidly, and the demand for polar cruise travel in the Northwest Passage continues to increase, while sea ice has long been a key factor limiting the development of polar cruise tourism. This study focuses on the operational risk of sea ice on cruise ships in the Northwest Passage (NWP), aiming to provide a scientific basis for ensuring the safety of cruise ship navigation and promoting the sustainable development of polar tourism. Based on ice data from 2015 to 2024, this study used the Polar Operational Limit Assessment Risk Indexing System (POLARIS) methodology recommended by the International Maritime Organization (IMO) to establish three scenarios for the route of ice class IC cruise ships: light ice, normal ice, and heavy ice. The navigable windows were systematically analyzed and critical waters along the route were identified. The results indicate that the navigable windows for IC ice-class cruise ships under light ice conditions are from mid-July to early December, while the navigable period under normal ice conditions is only from mid- to late September, and navigation is not possible under heavy ice conditions. The study identified Larsen Sound, Barrow Strait, Bellot Strait and Eastern Beaufort Sea as critical waters on the NWP cruise route. Among them, Larsen Sound and Eastern Beaufort Sea have a more prominent impact on voyage scheduling because their navigation weeks overlap less with other waters. This study provides a new idea for the risk assessment of polar cruise ships in ice regions. The research results can provide an important reference for the safe operation of polar cruise ships in the NWP and the decision-making of relevant parties.

In the wake of the continuous development of the international strategic petroleum reserve system, the tonnage and quantity of oil tankers have been increasing. This trend has driven the expansion of offshore oil exploration and transportation, resulting in frequent incidents of ship oil spills. Catastrophic impacts have been exerted on the marine environment by these accidents, posing a serious threat to economic development and ecological security. Therefore, there is an urgent need for efficient and reliable methods to detect oil spills in a timely manner and minimize potential losses as much as possible. In response to this challenge, a marine radar oil film segmentation method based on feature fusion and the artificial bee colony (ABC) algorithm is proposed in this study. Initially, the raw experimental data are preprocessed to obtain denoised radar images. Subsequently, grayscale adjustment and local contrast enhancement operations are carried out on the denoised images. Next, the gray level co-occurrence matrix (GLCM) features and Tamura features are extracted from the locally contrast-enhanced images. Then, the generalized least squares (GLS) method is employed to fuse the extracted texture features, yielding a new feature fusion map. Afterwards, the optimal processing threshold is determined to obtain effective wave regions by using the bimodal graph direct method. Finally, the ABC algorithm is utilized to segment the oil films. This method can provide data support for oil spill detection in marine radar images.