Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences
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 scienc...
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Hoboken, NJ
John Wiley & Sons, Inc
2021
John Wiley & Sons, Incorporated Wiley-Blackwell |
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| ISBN: | 9781119646143, 1119646146 |
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| Abstract | 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 adaptationAn exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registrationPractical discussions of regression, fitting, parameter retrieval, forecasting and interpolationAn 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. |
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| AbstractList | 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 adaptationAn exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registrationPractical discussions of regression, fitting, parameter retrieval, forecasting and interpolationAn 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. |
| Author | Tuia, Devis Zhu, Xiao Xiang Reichstein, Markus Camps-Valls, Gustau |
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| DOI | 10.1002/9781119646181 |
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| Notes | Includes bibliographical references and index |
| OCLC | 1264471512 |
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| Publisher | John Wiley & Sons, Inc John Wiley & Sons, Incorporated Wiley-Blackwell |
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| Snippet | 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... |
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| SubjectTerms | Algorithms-Study and teaching Earth sciences Earth sciences -- Data processing Earth sciences-Study and teaching Machine learning |
| TableOfContents | 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 17.2.2 Ice Sheet 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 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 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 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 |
| Title | Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences |
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