Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences
- Length: 432 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2021-08-16
- ISBN-10: 1119646146
- ISBN-13: 9781119646143
- Sales Rank: #763429 (See Top 100 Books)
DEEP LEARNING FOR THE EARTH SCIENCES
Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices
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.
The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of:
- An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation
- An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration
- Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation
- An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations
Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Cover Title Page Copyright Contents Foreword Acknowledgments List of Contributors List of Acronyms Chapter 1 Introduction 1.1 A Taxonomy of Deep Learning Approaches 1.2 Deep Learning in Remote Sensing 1.3 Deep Learning in Geosciences and Climate 1.4 Book Structure and Roadmap Part I Deep Learning to Extract Information from Remote Sensing Images Chapter 2 Learning Unsupervised Feature Representations of Remote Sensing Data with Sparse Convolutional Networks 2.1 Introduction 2.2 Sparse Unsupervised Convolutional Networks 2.2.1 Sparsity as the Guiding Criterion 2.2.2 The EPLS Algorithm 2.2.3 Remarks 2.3 Applications 2.3.1 Hyperspectral Image Classification 2.3.2 Multisensor Image Fusion 2.4 Conclusions Chapter 3 Generative Adversarial Networks in the Geosciences 3.1 Introduction 3.2 Generative Adversarial Networks 3.2.1 Unsupervised GANs 3.2.2 Conditional GANs 3.2.3 Cycle‐consistent GANs 3.3 GANs in Remote Sensing and Geosciences 3.3.1 GANs in Earth Observation 3.3.2 Conditional GANs in Earth Observation 3.3.3 CycleGANs in Earth Observation 3.4 Applications of GANs in Earth Observation 3.4.1 Domain Adaptation Across Satellites 3.4.2 Learning to Emulate Earth Systems from Observations 3.5 Conclusions and Perspectives Chapter 4 Deep Self‐taught Learning in Remote Sensing 4.1 Introduction 4.2 Sparse Representation 4.2.1 Dictionary Learning 4.2.2 Self‐taught Learning 4.3 Deep Self‐taught Learning 4.3.1 Application Example 4.3.2 Relation to Deep Neural Networks 4.4 Conclusion Chapter 5 Deep Learning‐based Semantic Segmentation in Remote Sensing 5.1 Introduction 5.2 Literature Review 5.3 Basics on Deep Semantic Segmentation: Computer Vision Models 5.3.1 Architectures for Image Data 5.3.2 Architectures for Point‐clouds 5.4 Selected Examples 5.4.1 Encoding Invariances to Train Smaller Models: The example of Rotation 5.4.2 Processing 3D Point Clouds as a Bundle of Images: SnapNet 5.4.3 Lake Ice Detection from Earth and from Space 5.5 Concluding Remarks Chapter 6 Object Detection in Remote Sensing 6.1 Introduction 6.1.1 Problem Description 6.1.2 Problem Settings of Object Detection 6.1.3 Object Representation in Remote Sensing 6.1.4 Evaluation Metrics 6.1.4.1 Precision‐Recall Curve 6.1.4.2 Average Precision and Mean Average Precision 6.1.5 Applications 6.2 Preliminaries on Object Detection with Deep Models 6.2.1 Two‐stage Algorithms 6.2.1.1 R‐CNNs 6.2.1.2 R‐FCN 6.2.2 One‐stage Algorithms 6.2.2.1 YOLO 6.2.2.2 SSD 6.3 Object Detection in Optical RS Images 6.3.1 Related Works 6.3.1.1 Scale Variance 6.3.1.2 Orientation Variance 6.3.1.3 Oriented Object Detection 6.3.1.4 Detecting in Large‐size Images 6.3.2 Datasets and Benchmark 6.3.2.1 DOTA 6.3.2.2 VisDrone 6.3.2.3 DIOR 6.3.2.4 xView 6.3.3 Two Representative Object Detectors in Optical RS Images 6.3.3.1 Mask OBB 6.3.3.2 RoI Transformer 6.4 Object Detection in SAR Images 6.4.1 Challenges of Detection in SAR Images 6.4.2 Related Works 6.4.3 Datasets and Benchmarks 6.5 Conclusion Chapter 7 Deep Domain Adaptation in Earth Observation 7.1 Introduction 7.2 Families of Methodologies 7.3 Selected Examples 7.3.1 Adapting the Inner Representation 7.3.2 Adapting the Inputs Distribution 7.3.3 Using (few, well‐chosen) Labels from the Target Domain 7.4 Concluding Remarks Chapter 8 Recurrent Neural Networks and the Temporal Component 8.1 Recurrent Neural Networks 8.1.1 Training RNNs 8.1.1.1 Exploding and Vanishing Gradients 8.1.1.2 Circumventing Exploding and Vanishing Gradients 8.2 Gated Variants of RNNs 8.2.1 Long Short‐term Memory Networks 8.2.1.1 The Cell State ct and the Hidden State ht 8.2.1.2 The Forget Gate ft 8.2.1.3 The Modulation Gate vt and the Input Gate it 8.2.1.4 The Output Gate ot 8.2.1.5 Training LSTM Networks 8.2.2 Other Gated Variants 8.3 Representative Capabilities of Recurrent Networks 8.3.1 Recurrent Neural Network Topologies 8.3.2 Experiments 8.4 Application in Earth Sciences 8.5 Conclusion Chapter 9 Deep Learning for Image Matching and Co‐registration 9.1 Introduction 9.2 Literature Review 9.2.1 Classical Approaches 9.2.2 Deep Learning Techniques for Image Matching 9.2.3 Deep Learning Techniques for Image Registration 9.3 Image Registration with Deep Learning 9.3.1 2D Linear and Deformable Transformer 9.3.2 Network Architectures 9.3.3 Optimization Strategy 9.3.4 Dataset and Implementation Details 9.3.5 Experimental Results 9.4 Conclusion and Future Research 9.4.1 Challenges and Opportunities 9.4.1.1 Dataset with Annotations 9.4.1.2 Dimensionality of Data 9.4.1.3 Multitemporal Datasets 9.4.1.4 Robustness to Changed Areas Chapter 10 Multisource Remote Sensing Image Fusion 10.1 Introduction 10.2 Pansharpening 10.2.1 Survey of Pansharpening Methods Employing Deep Learning 10.2.2 Experimental Results 10.2.2.1 Experimental Design 10.2.2.2 Visual and Quantitative Comparison in Pansharpening 10.3 Multiband Image Fusion 10.3.1 Supervised Deep Learning‐based Approaches 10.3.2 Unsupervised Deep Learning‐based Approaches 10.3.3 Experimental Results 10.3.3.1 Comparison Methods and Evaluation Measures 10.3.3.2 Dataset and Experimental Setting 10.3.3.3 Quantitative Comparison and Visual Results 10.4 Conclusion and Outlook Chapter 11 Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives 11.1 Introduction 11.2 Deep Learning for RS CBIR 11.3 Scalable RS CBIR Based on Deep Hashing 11.4 Discussion and Conclusion Acknowledgement Part II Making a Difference in the Geosciences With Deep Learning Chapter 12 Deep Learning for Detecting Extreme Weather Patterns 12.1 Scientific Motivation 12.2 Tropical Cyclone and Atmospheric River Classification 12.2.1 Methods 12.2.2 Network Architecture 12.2.3 Results 12.3 Detection of Fronts 12.3.1 Analytical Approach 12.3.2 Dataset 12.3.3 Results 12.3.4 Limitations 12.4 Semi‐supervised Classification and Localization of Extreme Events 12.4.1 Applications of Semi‐supervised Learning in Climate Modeling 12.4.1.1 Supervised Architecture 12.4.1.2 Semi‐supervised Architecture 12.4.2 Results 12.4.2.1 Frame‐wise Reconstruction 12.4.2.2 Results and Discussion 12.5 Detecting Atmospheric Rivers and Tropical Cyclones Through Segmentation Methods 12.5.1 Modeling Approach 12.5.1.1 Segmentation Architecture 12.5.1.2 Climate Dataset and Labels 12.5.2 Architecture Innovations: Weighted Loss and Modified Network 12.5.3 Results 12.6 Challenges and Implications for the Future 12.7 Conclusions Chapter 13 Spatio‐temporal Autoencoders in Weather and Climate Research 13.1 Introduction 13.2 Autoencoders 13.2.1 A Brief History of Autoencoders 13.2.2 Archetypes of Autoencoders 13.2.3 Variational Autoencoders (VAE) 13.2.4 Comparison Between Autoencoders and Classical Methods 13.3 Applications 13.3.1 Use of the Latent Space 13.3.1.1 Reduction of Dimensionality for the Understanding of the System Dynamics and its Interactions 13.3.1.2 Dimensionality Reduction for Feature Extraction and Prediction 13.3.2 Use of the Decoder 13.3.2.1 As a Random Sample Generator 13.3.2.2 Anomaly Detection 13.3.2.3 Use of a Denoising Autoencoder (DAE) Decoder 13.4 Conclusions and Outlook Chapter 14 Deep Learning to Improve Weather Predictions 14.1 Numerical Weather Prediction 14.2 How Will Machine Learning Enhance Weather Predictions? 14.3 Machine Learning Across the Workflow of Weather Prediction 14.4 Challenges for the Application of ML in Weather Forecasts 14.5 The Way Forward Chapter 15 Deep Learning and the Weather Forecasting Problem: Precipitation Nowcasting 15.1 Introduction 15.2 Formulation 15.3 Learning Strategies 15.4 Models 15.4.1 FNN‐based Models 15.4.2 RNN‐based Models 15.4.3 Encoder‐forecaster Structure 15.4.4 Convolutional LSTM 15.4.5 ConvLSTM with Star‐shaped Bridge 15.4.6 Predictive RNN 15.4.7 Memory in Memory Network 15.4.8 Trajectory GRU 15.5 Benchmark 15.5.1 HKO‐7 Dataset 15.5.2 Evaluation Methodology 15.5.3 Evaluated Algorithms 15.5.4 Evaluation Results 15.6 Discussion Appendix Acknowledgement Chapter 16 Deep Learning for High‐dimensional Parameter Retrieval 16.1 Introduction 16.2 Deep Learning Parameter Retrieval Literature 16.2.1 Land 16.2.2 Ocean 16.2.3 Cryosphere 16.2.4 Global Weather Models 16.3 The Challenge of High‐dimensional Problems 16.3.1 Computational Load of CNNs 16.3.2 Mean Square Error or Cross‐entropy Optimization? 16.4 Applications and Examples 16.4.1 Utilizing High‐dimensional Spatio‐spectral Information with CNNs 16.4.2 The Effect of Loss Functions in Retrieval of Sea Ice Concentrations 16.5 Conclusion Chapter 17 A Review of Deep Learning for Cryospheric Studies 17.1 Introduction 17.2 Deep‐learning‐based Remote Sensing Studies of the Cryosphere 17.2.1 Glaciers 17.2.2 Ice Sheet 17.2.3 Snow 17.2.4 Permafrost 17.2.5 Sea Ice 17.2.6 River Ice 17.3 Deep‐learning‐based Modeling of the Cryosphere 17.4 Summary and Prospect Appendix: List of Data and Codes Chapter 18 Emulating Ecological Memory with Recurrent Neural Networks 18.1 Ecological Memory Effects: Concepts and Relevance 18.2 Data‐driven Approaches for Ecological Memory Effects 18.2.1 A Brief Overview of Memory Effects 18.2.2 Data‐driven Methods for Memory Effects 18.3 Case Study: Emulating a Physical Model Using Recurrent Neural Networks 18.3.1 Physical Model Simulation Data 18.3.2 Experimental Design 18.3.3 RNN Setup and Training 18.4 Results and Discussion 18.4.1 The Predictive Capability Across Scales 18.4.2 Prediction of Seasonal Dynamics 18.5 Conclusions Part III Linking Physics and Deep Learning Models Chapter 19 Applications of Deep Learning in Hydrology 19.1 Introduction 19.2 Deep Learning Applications in Hydrology 19.2.1 Dynamical System Modeling 19.2.1.1 Large‐scale Hydrologic Modeling with Big Data 19.2.1.2 Data‐limited LSTM Applications 19.2.2 Physics‐constrained Hydrologic Machine Learning 19.2.3 Information Retrieval for Hydrology 19.2.4 Physically‐informed Machine Learning for Subsurface Flow and Reactive Transport Modeling 19.2.5 Additional Observations 19.3 Current Limitations and Outlook Acknowledgments Chapter 20 Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models 20.1 Introduction 20.2 The Parameterization Problem 20.3 Deep Learning Parameterizations of Subgrid Ocean Processes 20.3.1 Why DL for Subgrid Parameterizations? 20.3.2 Recent Advances in DL for Subgrid Parameterizations 20.4 Physics‐aware Deep Learning 20.5 Further Challenges ahead for Deep Learning Parameterizations Chapter 21 Deep Learning for the Parametrization of Subgrid Processes in Climate Models 21.1 Introduction 21.2 Deep Neural Networks for Moist Convection (Deep Clouds) Parametrization 21.3 Physical Constraints and Generalization 21.4 Future Challenges Chapter 22 Using Deep Learning to Correct Theoretically‐derived Models 22.1 Experiments with the Lorenz '96 System 22.1.1 The Lorenz'96 Equations and Coarse‐scale Models 22.1.1.1 Theoretically‐derived Coarse‐scale Model 22.1.1.2 Models with ANNs 22.1.2 Results 22.1.2.1 Single‐timestep Tendency Prediction Errors 22.1.2.2 Forecast and Climate Prediction Skill 22.1.3 Testing Seamless Prediction 22.2 Discussion and Outlook 22.2.1 Towards Earth System Modeling 22.2.2 Application to Climate Change Studies 22.3 Conclusion Chapter 23 Outlook Bibliography Index EULA
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