DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

IEEE Geoscience and Remote Sensing Magazine, Journal Year: 2023, Volume and Issue: 12(1), P. 138 - 161

Published: Dec. 28, 2023

In the last 40 years, remote sensing technology has evolved, significantly advancing ocean observation and catapulting its data into big era. How to efficiently accurately process analyze solve practical problems based on constitute a great challenge. Artificial intelligence (AI) developed rapidly in recent years. Numerous deep learning (DL) models have emerged, becoming prevalent analysis problem solving. Among these, convolutional neural networks (CNNs) stand as representative class of DL established themselves one premier solutions various research areas, including computer vision applications. this study, we first discuss model architectures CNNs some their variants well how they can be applied processing data. Then, demonstrate that fulfill most requirements for applications across following six categories: reconstruction 3D field, information extraction, image superresolution, phenomena forecast, transfer method, CNN interpretability method. Finally, technical challenges facing application CNN-based summarize future directions.

Language: Английский

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A review of artificial intelligence in marine science

Frontiers in Earth Science, Journal Year: 2023, Volume and Issue: 11

Published: Feb. 16, 2023

Utilization and exploitation of marine resources by humans have contributed to the growth research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied maritime research, complementing traditional forecasting models observation techniques some degree. This article takes algorithmic model as its starting point, references several application trials, methodically elaborates on emerging research trend mixing machine learning physical modeling concepts. discusses evolution methodologies for building ocean observations, remote sensing satellites, smart sensors, intelligent underwater robots, construction big data. We also cover method identifying internal waves (IW), heatwaves, El Niño-Southern Oscillation (ENSO), sea ice using algorithms. In addition, we analyze applications in prediction components, including physics-driven numerical models, model-driven statistical data-driven deep combined with models. review shows routes observation, phenomena identification, elements forecasting, examples forecasts their future development trends from angles points view, categorizing various uses sector.

Language: Английский

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Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Environments, Journal Year: 2023, Volume and Issue: 10(10), P. 170 - 170

Published: Oct. 2, 2023

This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest use satellites as innovative approach for monitoring quality, a critical step towards addressing challenges posed by rising anthropogenic pollution. Traditional methods have limitations, but satellite sensors provide potential solution that lowering costs expanding temporal spatial coverage. However, conventional statistical are limited when faced with formidable challenge conducting recognition analysis geospatial big data because they characterized high volume complexity. As compelling alternative, deep techniques has emerged indispensable tool, remarkable capability discern intricate patterns might otherwise remain elusive traditional statistics. study employed targeted search strategy, utilizing specific criteria titles 332 journal articles indexed Scopus, resulting inclusion 165 meta-analysis. Our comprehensive provides insights into trends, productivity, impact It highlights key journals publishers this domain while examining relationship between first author’s presentation, publication year, citation count, factor. major findings highlight widespread including MultiSpectral Instrument (MSI), Ocean Land Color (OLCI), Operational Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Plus (ETM+), practice multi-sensor fusion. Deep neural networks identified popular high-performing algorithms, significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer field learning. Chlorophyll-a clarity indicators receive special attention, geo-location had optical classes. contributes significantly providing extensive examples in-depth discussions code, well highlighting cyber infrastructure used research. Advances high-performance computing, large-scale processing capabilities, availability open-source software facilitating growing prominence applications artificial intelligence monitoring, positively contributing

Language: Английский

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Applications of Machine Learning in Chemical and Biological Oceanography

ACS Omega, Journal Year: 2023, Volume and Issue: 8(18), P. 15831 - 15853

Published: April 27, 2023

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses the use machine field chemical biological oceanography. In prediction global fixed nitrogen levels, partial carbon dioxide pressure, other properties, application promising tool. also utilized oceanography detect planktonic forms from images (i.e., microscopy, FlowCAM, video recorders), spectrometers, signal processing techniques. Moreover, successfully classified mammals using their acoustics, detecting endangered mammalian fish species specific environment. Most importantly, environmental data, proved be an effective method for predicting hypoxic conditions harmful algal bloom events, essential measurement terms monitoring. Furthermore, was used construct number databases will useful researchers, creation new help marine research community better comprehend chemistry biology ocean.

Language: Английский

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Integrated modeling framework to evaluate the impacts of multi-source water replenishment on lacustrine phytoplankton communities

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 612, P. 128272 - 128272

Published: Aug. 8, 2022

Language: Английский

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Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal

Marine Pollution Bulletin, Journal Year: 2023, Volume and Issue: 199, P. 115945 - 115945

Published: Dec. 27, 2023

Language: Английский

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Algal blooms forecasting with hybrid deep learning models from satellite data in the Zhoushan fishery

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102664 - 102664

Published: June 6, 2024

Algal blooms are increasingly frequent in coastal areas, posing a significant threat to ecosystems. The Zhoushan fishery, one of the most affected regions along Chinese coast, faces severe challenges from algal blooms. In this study, Convolutional Neural Network (CNN), Long Short-term Memory (LSTM) and hybrid CNN-LSTM deep learning models were constructed forecast chlorophyll (Chl) concentrations satellite data. model outperformed individual models, achieving highest determination coefficient lowest root mean square error for Chl concentration forecasts. It also excelled predicting blooms, with probability detection Heidke skill score, effectively capturing trends bloom development. areas high concentration, parameter significantly influences forecasts, while meridional wind current main influence factors medium low concentration. powerful provided by offers valuable support efficient management sustainable development fishery.

Language: Английский

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3LATNet: Attention based deep learning model for global Chlorophyll-a retrieval from GCOM-C satellite

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 220, P. 490 - 508

Published: Jan. 10, 2025

Language: Английский

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Development of a core feature identification application based on the Faster R-CNN algorithm

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 115, P. 105200 - 105200

Published: Aug. 1, 2022

Language: Английский

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Exploring the Learning Psychology Mobilization of Music Majors Through Innovative Teaching Methods Under the Background of New Curriculum Reform

Frontiers in Psychology, Journal Year: 2022, Volume and Issue: 12

Published: Jan. 21, 2022

The research expects to explore the psychological mobilization of innovative teaching methods Music Majors under new curriculum reform. relevant theories college students' are analyzed deep learning together with innovation and construction music courses. Thereupon, is studied. Firstly, relationship between entrepreneurship obtained through a literature review. Secondly, classroom model designed based on theory, four dimensions defined innovate optimize model. Finally, Questionnaire Survey (QS) used analyze design Only 15% 180 respondents understand concept learning, 32% like interactive 36% competitive comparative learning. And students who study instrumental have higher significant differences in motivation than those vocal music. In addition 16% people improve their skills equipment. College classes comparison that can give more play subjective initiative. After reform, stimulate interest participate psychology. Therefore, future education teaching, there need pay attention status. results provide references practical significance for activities classrooms after

Language: Английский

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