The increase in maritime traffic and vessel size has strengthened the need for economical and safe maritime transportation networks. Currently, ship path planning is based on past experience and shortest route usage. However, the increasing complexity of the marine environment and the development of autonomous ships require automatic shortest path generation based on maritime traffic networks. This paper proposes an efficient shortest path planning method using Dijkstra’s algorithm based on a maritime traffic network dataset created by extracting maritime traffic routes through a spatial-temporal density analysis of large-scale AIS data and Delaunay triangulation. Additionally, the depth information of all digital charts in Korea was set as a safety contour to support safe path planning. The proposed network-based shortest path planning method was compared with the path planning and sailing distance of a training ship, and compliance with maritime laws was verified. The results demonstrate the practicality and safety of the proposed method, which can enable the establishment of a safe and efficient maritime transportation network along with the development of autonomous ships.

Benthic habitat zonation is described from in situ observations and seabed photographs taken from the coastal area of Weno Island, Chuuk, Micronesia. Habitat groups, types, and boundaries are defined by visible substratum characteristics (i.e., in situ and by digital imaging of photo-quadrats along transect lines), and by cluster and ordination analyses using relative coverage percentage of identified classification categories. The statistical similarity between habitat groups is determined by Analysis of similarity (ANOSIM). Benthic habitat groups with significant influence on the determination of habitat type are isolated by the similarity percentage (SIMPER) test. In addition to the standard practices of using transect lines and collecting data in accordance with the already well-implemented and thoroughly-tested benthic habitat classification scheme for tropical reef monitoring, we applied simple statistics that enable comparative data interpretation. Our simple, repeatable methods provide a framework for benthic habitat-related monitoring research that allows the comparison of results across regions.

Coastal debris is a global environmental issue that requires systematic monitoring strategies based on reliable statistical data. Recent advances in remote sensing and deep learning-based object detection have enabled the development of efficient coastal debris monitoring systems. In this study, two state-of-the-art object detection models—RT-DETR and YOLOv10—were applied to UAV-acquired images for coastal debris detection. Their false positive characteristics were analyzed to provide guidance on model selection under different coastal environmental conditions. Quantitative evaluation using mean average precision (mAP@0.5) showed comparable performance between the two models (RT-DETR: 0.945, YOLOv10: 0.957). However, bounding box label accuracy revealed a significant gap, with RT-DETR achieving 80.18% and YOLOv10 only 53.74%. Class-specific analysis indicated that both models failed to detect Metal and Glass and showed low accuracy for fragmented debris, while buoy-type objects with high structural integrity (Styrofoam Buoy, Plastic Buoy) were consistently identified. Error analysis further revealed that RT-DETR tended to overgeneralize by misclassifying untrained objects as similar classes, whereas YOLOv10 exhibited pronounced intra-class confusion in fragment-type objects. These findings demonstrate that mAP alone is insufficient to evaluate model performance in real-world coastal monitoring. Instead, model assessment should account for training data balance, coastal environmental characteristics, and UAV imaging conditions. Future studies should incorporate diverse coastal environments and apply dataset augmentation to establish statistically robust and standardized monitoring protocols for coastal debris.

The majority of ocean pollution stems from terrestrial sources, with more than 80% attributed to chemicals, industrial waste, toxic metals, plastics, sewage, and other land-based materials. Therefore, the management of ocean pollution must initiate from terrestrial interventions, necessitating the control of pollutants discharged from land sources and the crucial pursuit of identifying the roots of ocean pollution. Identifying the causes of ocean pollution enables the exploration of mitigation strategies for the entities responsible for pollution generation and facilitates the formulation of relevant policies. Furthermore, it can contribute to imposing costs for purification and raising awareness about the seriousness of ocean pollution. However, Rivers and streams that lead to the ocean are influenced by various changing factors as they pass through the land, making it a globally challenging task to pinpoint the sources of pollution. Accordingly, in this study, we collected water quality observation data measured at five points in a specific estuary area and water quality data from five areas near the source of pollution, and conducted a study to calculate the contribution of pollution from the source. We performed statistical analysis and machine learning-based pollution source analysis, and developed an improved artificial intelligence model proposed in this study that complements the limitations of existing analysis methods. The variables used in the analysis were POC average concentration, POC δ13C, PN average concentration, PN δ15N, average concentration, and DOC δ13C. For basic data analysis, data distribution analysis, similarity/discrimination analysis, and clustering analysis of variables by branch were performed. Basic data analysis allowed for dividing the data's characteristics into four groups, but discrepancies in similarities emerged among items based on each analysis method, limiting the meaningfulness of the data analysis. For this, we analyzed which pollutants contaminated the five points in the river estuary using machine learning techniques such as XGBoost and a deep learning neural network, an artificial neural network model. The XGBoost analysis categorized pollution sources for each location into 1 to 2 categories, showing accuracies ranging from 51.04% to 99.92%. However, due to the intrinsic nature of machine learning, predicted values tend to maximize similarity to the most similar pollutant source, resulting in extreme values exceeding 99%. The artificial neural network analysis resulted in the classification of 2 or more pollution sources for each location, with accuracies ranging from 33.23% to 62.45%. This is considered a result of relatively lower accuracy due to the unique characteristics of each location. To overcome the limitations of each model, this study created an integrated model that aggregates results from multiple models to determine the similarity. The analysis using the integrated model effectively identified pollution sources excellently without encountering extreme accuracy issues. To ensure the reliability of future pollution contribution assessment models, determining pollution contribution through isotopic fraction analysis will be necessary.

The environmental issues caused by marine debris and the problem of habitat pollution for marine organisms are pervasive worldwide. Both floating debris and sunken debris contaminate various habitats, including coastlines, coral reefs, and seaweed beds. Various marine organisms exposed to such marine debris ultimately suffer from entanglement and ingestion, with sea turtles, in particular, accounting for 66% of reported cases of harm among all marine mammals. In Korea, various cases of mortality due to entanglement and ingestion in sea turtles have been widely reported. To comprehend the correlation between the behavior, habitats, and marine debris associated with sea turtles, ecological research is being conducted through location tracking. it is essential to conduct habitat degradation research for sea turtles by analyzing their spatial behavior using location-based methods and understanding feeding patterns using various environmental information. To address these issues, it is crucial to accurately understand the movement routes and activity patterns of marine organisms. In the field of wildlife research, various studies are being conducted using geographic information systems to utilize diverse analytical methods. In this study, we aimed to develop a web-responsive analysis tool for continuous tracking of sea turtle behavior and habitat foraging. The analysis module comprises three parts: the preprocessing module, spatial analysis module, and exploratory analysis module. The preprocessing module functions to extract necessary data from Argos satellite-received location information and refine it into clean data. It extracts latitude, longitude, sea surface temperature, and depth information from multiple files, organizes them into a single table, and saves them in a analyzable file format. The analysis module includes functions for deriving sea turtle activity ranges and overlapping analyses of habitat within activity zones. The activity range analysis utilizes Kernel Density Estimation (KDE) based on sea turtle location point data. Bandwidth, defined automatically based on the distribution of accumulation and points, allows for efficient analysis. The habitat overlapping analysis integrates various biological occurrence information such as coral, algae, and jellyfish within the sea turtle's activity zone. This enables exploration of the sea turtle's habitat environment within dense areas. The exploratory analysis module offers visualization features for location information, received depth, and sea surface temperature derived from data received by Argos satellites. Depth and sea surface temperature details are presented alongside location information, utilizing color coding for enhanced comprehension. The analysis module and the platform it is implemented on were developed in the form of a responsive web application using the open-source R-shiny. The responsive web application allows researchers to input and analyze sea turtle location data directly from a web page in any internet-enabled environment. It is fast and efficient as the results can be promptly visualized on a map. The sea turtle behavioral analysis tool developed in this study enables researchers to obtain standardized information related to behavior and habitat using location-based sea turtle data received from various satellites. It establishes a systematic approach for researchers to easily utilize this information through the web.

Deep learning-based object detection models, such as YOLO and DETR, have been actively studied for monitoring coastal debris. While recent models exhibit minimal differences in quantitative accuracy and performance, the underlying algorithms and methodologies for object detection vary across models. Consequently, detection outcomes can differ based on the type of the debris and the characteristics of the coastal environment. Nonetheless, there is a notable lack of studies that provide a quantitative analysis of these findings. Therefore, this study analyzed the false positives of coastal debris using the YOLOv10 and RT-DETR models to identify the detection characteristics of each model. To ensure comparable performance between the two models, hyperparameters were fine-tuned to achieve a mean Average Precision (mAP) exceeding 0.9. A dataset of approximately 350,000 coastal debris images (sourced from https://www.aihub.or.kr/) was utilized to train both models, with an 8:2 split between training and validation sets. Coastal debris was classified into 11 categories: Glass, Metal, Net, PET Bottle, Plastic Buoy, Plastic ETC, Plastic Buoy of China, Rope, Styrofoam Box, Styrofoam Buoy, and Styrofoam Piece. To analyze the detection characteristics of the trained models, images of coastal with various types of debris were collected using UAVs. False positive objects were classified and systematically analyzed based on the detection results of the collected coastal debris images using the two model. The analysis of false positives revealed that the YOLOv10 model exhibited a 72% false positive rate for Styrofoam buoys, attributed primarily to the significant impact of object color and shape. In the RT-DETR model, false positive rates were observed at 22% for seaweed and 20% for Styrofoam buoys, with object color and surface composition as key contributing factors. Based on these findings, it is recommended to consider the characteristics of the coastal and the distributed debris when selecting a deep learning model for coastal debris detection. Future studies on precise classification of coastal debris and diverse environmental data will facilitate the selection of optimal deep learning models for specific field conditions.