Singapore: Springer, 2023. — 351 p. — ISBN 978-981-19-6375-9.
This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.
Artificial Intelligence Foundation of Smart Ocean
Forecasting Tropical Instability Waves Based on Artificial Intelligence
Sea Surface Height Anomaly Prediction Based on Artificial Intelligence
Satellite Data-Driven Internal Solitary Wave Forecast Based on Machine Learning Techniques
AI-Based Subsurface Thermohaline Structure Retrieval from Remote Sensing Observations
Ocean Heat Content Retrieval from Remote Sensing Data Based on Machine Learning
Detecting Tropical Cyclogenesis Using Broad Learning System from Satellite Passive Microwave Observations
Tropical Cyclone Monitoring Based on Geostationary Satellite Imagery
Reconstruction of pCO2 Data in the Southern Ocean Based on Feedforward Neural Network
Detection and Analysis of Mesoscale Eddies Based on Deep Learning2
Deep Convolutional Neural Networks-Based Coastal Inundation Mapping from SAR Imagery: ith One Application Case for Bangladesh, a UN-defined Least Developed Country
Sea Ice Detection from SAR Images Based on Deep Fully Convolutional Networks
Detection and Analysis of Marine Green Algae Based on Artificial Intelligence
Automatic Waterline Extraction of Large-Scale Tidal Flats from SAR Images Based on Deep Convolutional Neural Networks
Extracting Ship’s Size from SAR Images by Deep Learning
Benthic Organism Detection, Quantification and Seamount Biology Detection Based on Deep Learning