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.
Topics of interest include, but are not limited to:
• Best practices for the application of deep learning in marine/underwater object detection/classification of marine organisms (plankton, fish, and coral reef, etc.) and marine environments (trash and debris, etc.).
• Deep learning for optical/acoustic image enhancement in ocean observation/exploration (marine organism, seafloor, etc.).
• Deep learning for detection, classification, localization, and density estimation of marine organisms (fish and marine mammals) with underwater optical/acoustic data acquisition technology.
• Deep learning for detection, classification, and segmentation of objects and phenomena in the ocean from the data of remote sensing and drone surveys.
• Research on new datasets for underwater/marine optical/acoustic data analysis.
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.
Topics of interest include, but are not limited to:
• Best practices for the application of deep learning in marine/underwater object detection/classification of marine organisms (plankton, fish, and coral reef, etc.) and marine environments (trash and debris, etc.).
• Deep learning for optical/acoustic image enhancement in ocean observation/exploration (marine organism, seafloor, etc.).
• Deep learning for detection, classification, localization, and density estimation of marine organisms (fish and marine mammals) with underwater optical/acoustic data acquisition technology.
• Deep learning for detection, classification, and segmentation of objects and phenomena in the ocean from the data of remote sensing and drone surveys.
• Research on new datasets for underwater/marine optical/acoustic data analysis.
