Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Preparing for a Northwest Passage: A Workshop on the Role of New England in Navigating the New Arctic

Preparing for a Northwest Passage: A Workshop on the Role of New England in Navigating the New Arctic (March 25 - 27, 2018 -- The University of New Hampshire) paired two of NSF\u27s 10 Big Ideas: Navigating the New Arctic and Growing Convergence Research at NSF. During this event, participants assessed economic, environmental, and social impacts of Arctic change on New England and established convergence research initiatives to prepare for, adapt to, and respond to these effects. Shipping routes through an ice-free Northwest Passage in combination with modifications to ocean circulation and regional climate patterns linked to Arctic ice melt will affect trade, fisheries, tourism, coastal ecology, air and water quality, animal migration, and demographics not only in the Arctic but also in lower latitude coastal regions such as New England. With profound changes on the horizon, this is a critical opportunity for New England to prepare for uncertain yet inevitable economic and environmental impacts of Arctic change

Streaming Scene Maps for Co-Robotic Exploration in Bandwidth Limited Environments

This paper proposes a bandwidth tunable technique for real-time probabilistic scene modeling and mapping to enable co-robotic exploration in communication constrained environments such as the deep sea. The parameters of the system enable the user to characterize the scene complexity represented by the map, which in turn determines the bandwidth requirements. The approach is demonstrated using an underwater robot that learns an unsupervised scene model of the environment and then uses this scene model to communicate the spatial distribution of various high-level semantic scene constructs to a human operator. Preliminary experiments in an artificially constructed tank environment as well as simulated missions over a 10m$\times$10m coral reef using real data show the tunability of the maps to different bandwidth constraints and science interests. To our knowledge this is the first paper to quantify how the free parameters of the unsupervised scene model impact both the scientific utility of and bandwidth required to communicate the resulting scene model.Comment: 8 pages, 6 figures, accepted for presentation in IEEE Int. Conf. on Robotics and Automation, ICRA '19, Montreal, Canada, May 201

Survey on video anomaly detection in dynamic scenes with moving cameras

The increasing popularity of compact and inexpensive cameras, e.g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras. However, existing reviews primarily concentrate on Video Anomaly Detection (VAD) methods assuming static cameras. The VAD literature with moving cameras remains fragmented, lacking comprehensive reviews to date. To address this gap, we endeavor to present the first comprehensive survey on Moving Camera Video Anomaly Detection (MC-VAD). We delve into the research papers related to MC-VAD, critically assessing their limitations and highlighting associated challenges. Our exploration encompasses three application domains: security, urban transportation, and marine environments, which in turn cover six specific tasks. We compile an extensive list of 25 publicly-available datasets spanning four distinct environments: underwater, water surface, ground, and aerial. We summarize the types of anomalies these datasets correspond to or contain, and present five main categories of approaches for detecting such anomalies. Lastly, we identify future research directions and discuss novel contributions that could advance the field of MC-VAD. With this survey, we aim to offer a valuable reference for researchers and practitioners striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie

The seagoing scientist's toolbox: integrated methods for quality control of marine geophysical data at sea

We announce a new and integrated system for planning and executing marine geophysical surveys and for scrutinizing and visualizing incoming shipboard data. The system incorporates free software designed for use by scientists and shipboard operators and pertains to underway geophysics and multibeam sonar surveys. Regarding underway data, a crucial first step in the approach is to reduce and merge incoming center beam depth, gravity, and towed magnetic data with navigation, then reformat to the standard exchange format. We are then able to apply established quality control methods including along-track and cross-track analyses to identify error sources and to incrementally build the candidate archive file as new data are acquired. Regarding multibeam data, these are subjected to both an automated error removal scheme for quick visualization and to subsequent ping editing in detail. The candidate archive file and sonar data are automatically and periodically updated and adapted for display in Google Earth, wherein survey planning is also carried out. Data layers are also updated automatically in Google Earth, allowing scientists to focus on visual inspection and interpretation of incoming data. By visualizing underway and sonar data together with reference gravity, magnetic, and bathymetry grids in Google Earth, data familiarity is enhanced and the likelihood of noticing extreme errors increased. We hope scientists will embrace these techniques so that each data set being submitted to a data repository is vetted by the seagoing science party.U.S. National Science FoundationNational Science Foundation (NSF) [1458964, 1558403]Korea Institute of Ocean Science and Technology [PM59941, PM60321

Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews

Spatio-Temporal Deep Learning Approaches for Addressing Track Association Problem using Automatic Identification System (AIS) Data

In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the Automatic Identification System, which serves as a communication medium between neighboring ships and the control center. While traditional methods have relied on sequential tracking and physics-based models, the emergence of deep learning has significantly transformed techniques typically used in trajectory prediction, clustering, and anomaly detection. This transformation is largely attributed to the deep learning algorithm’s capability to model complex nonlinear relationships while capturing both the spatial and temporal dynamics of ship movement. Capitalizing on this computational advantage, our study focuses on evaluating different deep learning architectures such as Multi Model Long Short-Term Memory (LSTM), 1D Convolutional-LSTM, and Temporal-Graph Convolutional Neural Networks— in addressing the problem of track association. The performance of these proposed models are compared against different deep learning algorithms specialized in track association tasks using several real-life AIS datasets

Division of Research and Economic Development Annual Report for FY2002

Annual report for the Division of Research and Economic Development of the University of Rhode Island for the year 2001-2002. Includes statistics of project proposals, expenditures, URI Foundation Awards, previous annual report summaries and awards received by individual academic and administrative departments