2,495 research outputs found
Ocean Eddy Identification and Tracking using Neural Networks
Global climate change plays an essential role in our daily life. Mesoscale
ocean eddies have a significant impact on global warming, since they affect the
ocean dynamics, the energy as well as the mass transports of ocean circulation.
From satellite altimetry we can derive high-resolution, global maps containing
ocean signals with dominating coherent eddy structures. The aim of this study
is the development and evaluation of a deep-learning based approach for the
analysis of eddies. In detail, we develop an eddy identification and tracking
framework with two different approaches that are mainly based on feature
learning with convolutional neural networks. Furthermore, state-of-the-art
image processing tools and object tracking methods are used to support the eddy
tracking. In contrast to previous methods, our framework is able to learn a
representation of the data in which eddies can be detected and tracked in more
objective and robust way. We show the detection and tracking results on sea
level anomalies (SLA) data from the area of Australia and the East Australia
current, and compare our two eddy detection and tracking approaches to identify
the most robust and objective method.Comment: accepted for International Geoscience and Remote Sensing Symposium
201
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
Coastal high-frequency radars in the Mediterranean ??? Part 2: Applications in support of science priorities and societal needs
International audienceThe Mediterranean Sea is a prominent climate-change hot spot, with many socioeconomically vital coastal areas being the most vulnerable targets for maritime safety, diverse met-ocean hazards and marine pollution. Providing an unprecedented spatial and temporal resolution at wide coastal areas, high-frequency radars (HFRs) have been steadily gaining recognition as an effective land-based remote sensing technology for continuous monitoring of the surface circulation, increasingly waves and occasionally winds. HFR measurements have boosted the thorough scientific knowledge of coastal processes, also fostering a broad range of applications, which has promoted their integration in coastal ocean observing systems worldwide, with more than half of the European sites located in the Mediterranean coastal areas. In this work, we present a review of existing HFR data multidisciplinary science-based applications in the Mediterranean Sea, primarily focused on meeting end-user and science-driven requirements, addressing regional challenges in three main topics: (i) maritime safety, (ii) extreme hazards and (iii) environmental transport process. Additionally, the HFR observing and monitoring regional capabilities in the Mediterranean coastal areas required to underpin the underlying science and the further development of applications are also analyzed. The outcome of this assessment has allowed us to provide a set of recommendations for future improvement prospects to maximize the contribution to extending science-based HFR products into societally relevant downstream services to support blue growth in the Mediterranean coastal areas, helping to meet the UN's Decade of Ocean Science for Sustainable Development and the EU's Green Deal goals
Coastal high-frequency radars in the Mediterranean - Part 2: Applications in support of science priorities and societal needs
The Mediterranean Sea is a prominent climate-change hot spot, with many socioeconomically vital coastal areas being the most vulnerable targets for maritime safety, diverse met-ocean hazards and marine pollution. Providing an unprecedented spatial and temporal resolution at wide coastal areas, high-frequency radars (HFRs) have been steadily gaining recognition as an effective land-based remote sensing technology for continuous monitoring of the surface circulation, increasingly waves and occasionally winds. HFR measurements have boosted the thorough scientific knowledge of coastal processes, also fostering a broad range of applications, which has promoted their integration in coastal ocean observing systems worldwide, with more than half of the European sites located in the Mediterranean coastal areas. In this work, we present a review of existing HFR data multidisciplinary science-based applications in the Mediterranean Sea, primarily focused on meeting end-user and science-driven requirements, addressing regional challenges in three main topics: (i) maritime safety, (ii) extreme hazards and (iii) environmental transport process. Additionally, the HFR observing and monitoring regional capabilities in the Mediterranean coastal areas required to underpin the underlying science and the further development of applications are also analyzed. The outcome of this assessment has allowed us to provide a set of recommendations for future improvement prospects to maximize the contribution to extending science-based HFR products into societally relevant downstream services to support blue growth in the Mediterranean coastal areas, helping to meet the UN's Decade of Ocean Science for Sustainable Development and the EU's Green Deal goals
On the Exploitation of Multimodal Remote Sensing Data Combination for Mesoscale/Submesoscale Eddy Detection in the Marginal Ice Zone
The detection and analysis of ocean eddies via remote sensing have become a hot topic in physical oceanography during the last few decades. However, eddy identification and tracking via remote sensing can be a challenging task, since each sensor has some limitations. In order to overcome potential challenges, it is crucial to exploit the complementary information provided by different sensing systems. As one of the steps toward this aim, we have investigated the pertinence of applying the scheme, including a texture features extraction and a superpixel segmentation method, in order to distinguish eddies in the marginal ice zone (MIZ) using multisensor remote sensing data. Nevertheless, not all the images available from various sensors are of actual importance, since they can be corrupted, redundant, or simply unnecessary for a particular task. Therefore, we are additionally exploring the relevance of different sensors separately and simultaneously as well as with extracted texture features for eddy monitoring
Remote Sensing of the Oceans
This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements
Identification of vortex in unstructured mesh with graph neural networks
Deep learning has been employed to identify flow characteristics from
Computational Fluid Dynamics (CFD) databases to assist the researcher to better
understand the flow field, to optimize the geometry design and to select the
correct CFD configuration for corresponding flow characteristics. Convolutional
Neural Network (CNN) is one of the most popular algorithms used to extract and
identify flow features. However its use, without any additional flow field
interpolation, is limited to the simple domain geometry and regular meshes
which limits its application to real industrial cases where complex geometry
and irregular meshes are usually used. Aiming at the aforementioned problems,
we present a Graph Neural Network (GNN) based model with U-Net architecture to
identify the vortex in CFD results on unstructured meshes. The graph generation
and graph hierarchy construction using algebraic multigrid method from CFD
meshes are introduced. A vortex auto-labeling method is proposed to label
vortex regions in 2D CFD meshes. We precise our approach by firstly optimizing
the input set on CNNs, then benchmarking current GNN kernels against CNN model
and evaluating the performances of GNN kernels in terms of classification
accuracy, training efficiency and identified vortex morphology. Finally, we
demonstrate the adaptability of our approach to unstructured meshes and
generality to unseen cases with different turbulence models at different
Reynolds numbers.Comment: Accepted by the journal Computers & Fluid
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