Deep Learning For Marine Science
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Deep Learning for Marine Science
Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.
Deep Learning for Marine Science, volume II
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.
Into the Deep
Containing 97 percent of Earth's water supply, the ocean plays a huge role in regulating global temperatures, supporting plant and animal life, and contributing to the livelihoods of millions of people. But in spite of all this, the ocean remains drastically unexplored, and the details of its impact on human lives aren't fully understood. Scientists from around the world are realizing that to address issues plaguing the ocean, such as dead zones, coral bleaching, and climate change, we need to better understand this incredible, unique feature of our planet. With a range of impressive, cutting-edge technologies at their disposal, oceanographers have set out to measure, sample, and analyze at every turn. Every day, mysteries about the ocean are being solved, and every day, new questions come to light. The more scientists learn, the better they are able to answer these new questions. What lies in the deep? And who is at the forefront of these exciting discoveries? The scientists and research included in this book shed light on the most pressing issues currently facing oceanographers and point us in the right direction to solving these challenges.