Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research.
Introduction
Learning Unsupervised Feature Representations of Remote Sensing Data with Sparse Convolutional Networks
Generative Adversarial Networks in the Geosciences
Deep Self-taught Learning in Remote Sensing
Deep Learning-based Semantic Segmentation in Remote Sensing
Object Detection in Remote Sensing
Deep Domain Adaptation in Earth Observation
Recurrent Neural Networks and the Temporal Component
Deep Learning for Image Matching and Co-registration
Multisource Remote Sensing Image Fusion
Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives
Deep Learning for Detecting Extreme Weather Patterns
Spatio-temporal Autoencoders in Weather and Climate Research
Deep Learning to Improve Weather Predictions
Deep Learning and the Weather Forecasting Problem: Precipitation Nowcasting
Deep Learning for High-dimensional Parameter Retrieval
A Review of Deep Learning for Cryospheric Studies
Emulating Ecological Memory with Recurrent Neural Networks
Applications of Deep Learning in Hydrology
Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models
Deep Learning for the Parametrization of Subgrid Processes in Climate Models
Using Deep Learning to Correct Theoretically-derived Models
Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research.