This Research Topic is the second volume of this collection. You can find the original collection via https://www.frontiersin.org/research-topics/45485/deep-learning-for-marine-science
Deep learning (DL) is a critical research branch in the fields of artificial intelligence and machine learning, encompassing various technologies such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformer networks and Diffusion models, as well as self-supervised learning (SSL) and reinforcement learning (RL). These technologies have been successfully applied to scientific research and numerous aspects of daily life. With the continuous advancements in oceanographic observation equipment and technology, there has been an explosive growth of ocean data, propelling marine science into the era of big data. As effective tools for processing and analyzing large-scale ocean data, DL techniques have great potential and broad application prospects in marine science. Applying DL to intelligent analysis and exploration of research data in marine science can provide crucial support for various domains, including meteorology and climate, environment and ecology, biology, energy, as well as physical and chemical interactions. Despite the significant progress in DL, its application to the aforementioned marine science domains is still in its early stages, necessitating the full utilization and continuous exploration of representative applications and best practices.
The goal of this research topic is to explore the application of mature DL techniques, including CNN, RNN, Transformer, Diffusion, SSL, RL, etc., in the field of marine science. This includes the intelligent analysis and investigation of research data related to numerical models, observation data, acoustic/optical sensing and detection data, and satellite remote sensing data. These applications encompass a wide range of areas within marine science, such as ocean/climate/weather forecasting, numerical solution acceleration of physical ocean equations, detection and identification of the processes and phenomena in the ocean and atmosphere, as well as intelligent processing and identification of marine biology, environment, and topography information.
• DL for the application research of ocean, climate, weather, and related environmental elements forecasting based on the data of numerical models and observation technologies.
• DL for the identification, detection, and prediction of dynamic processes and environmental phenomena in the ocean and atmosphere based on the data of numerical models and observation.
• DL in the recognition, detection, and image enhancement of marine/underwater objects by using acoustic, optical, and remote sensing technologies, including marine organisms (plankton, fish, etc.), marine environment (marine trash, debris, etc.), and topography, etc.
• DL in the numerical solution acceleration of physical ocean equations (Navier-Stokes equation, convection-diffusion equation, etc.) using coupling techniques of data-driven methods and traditional numerical methods.
• Research on marine science related datasets involving the aforementioned research topics.
This Research Topic is the second volume of this collection. You can find the original collection via https://www.frontiersin.org/research-topics/45485/deep-learning-for-marine-science
Deep learning (DL) is a critical research branch in the fields of artificial intelligence and machine learning, encompassing various technologies such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformer networks and Diffusion models, as well as self-supervised learning (SSL) and reinforcement learning (RL). These technologies have been successfully applied to scientific research and numerous aspects of daily life. With the continuous advancements in oceanographic observation equipment and technology, there has been an explosive growth of ocean data, propelling marine science into the era of big data. As effective tools for processing and analyzing large-scale ocean data, DL techniques have great potential and broad application prospects in marine science. Applying DL to intelligent analysis and exploration of research data in marine science can provide crucial support for various domains, including meteorology and climate, environment and ecology, biology, energy, as well as physical and chemical interactions. Despite the significant progress in DL, its application to the aforementioned marine science domains is still in its early stages, necessitating the full utilization and continuous exploration of representative applications and best practices.
The goal of this research topic is to explore the application of mature DL techniques, including CNN, RNN, Transformer, Diffusion, SSL, RL, etc., in the field of marine science. This includes the intelligent analysis and investigation of research data related to numerical models, observation data, acoustic/optical sensing and detection data, and satellite remote sensing data. These applications encompass a wide range of areas within marine science, such as ocean/climate/weather forecasting, numerical solution acceleration of physical ocean equations, detection and identification of the processes and phenomena in the ocean and atmosphere, as well as intelligent processing and identification of marine biology, environment, and topography information.
• DL for the application research of ocean, climate, weather, and related environmental elements forecasting based on the data of numerical models and observation technologies.
• DL for the identification, detection, and prediction of dynamic processes and environmental phenomena in the ocean and atmosphere based on the data of numerical models and observation.
• DL in the recognition, detection, and image enhancement of marine/underwater objects by using acoustic, optical, and remote sensing technologies, including marine organisms (plankton, fish, etc.), marine environment (marine trash, debris, etc.), and topography, etc.
• DL in the numerical solution acceleration of physical ocean equations (Navier-Stokes equation, convection-diffusion equation, etc.) using coupling techniques of data-driven methods and traditional numerical methods.
• Research on marine science related datasets involving the aforementioned research topics.