Deep learning for the earth sciences: a comprehensive approach to remote sensing, climate science and geosciences
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 proli...
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| Zusammenfassung: | 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. |
| Beschreibung: | xxxvi, 405 Seiten Illustrationen, Diagramme, Karten (schwarz-weiß) |
| ISBN: | 9781119646143 |
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| 245 | 1 | 0 | |a Deep learning for the earth sciences |b a comprehensive approach to remote sensing, climate science and geosciences |c edited by Gustau Camps-Valls (Universitat de València, Spain), Devis Tuia (EPFL, Switzerland), Xiao Xiang Zhu (German Aerospace Center and Technical University of Munich, Germany), Markus Reichstein (Max Planck Institute, Germany) |
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| 520 | 3 | |a 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. | |
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Datensatz im Suchindex
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| adam_text | Contents Foreword xvi Acknowledgments xvii List of Contributors xviii List of Acronyms xxiv 1 1.1 1.2 1.3 1.4 1 Gustau Camps-Valls, Xiao Xiang Zhu, Devis Tuia, and Markus Reichstein A Taxonomy of Deep Learning Approaches 2 Deep Learning in Remote Sensing 3 Deep Learning in Geosciences and Climate 7 Book Structure and Roadmap 9 Introduction Part I 2 2.1 2.2 2.2.1 2.2.2 2.2.3 2.3 2.3.1 2.3.2 2.4 Deep Learning to Extract Information from Remote Sensing Images 13 Learning Unsupervised Feature Representations of Remote Sensing Data with Sparse Convolutional Networks 15 Jose E. Adsuara, Manuel Campos-Taberner, Javier Garcia-Haro, Carlo Gatta, Adriana Romero, and Gustau Camps-Valls Introduction 15 Sparse Unsupervised Convolutional Networks 17 Sparsity as the Guiding Criterion 17 The EPLS Algorithm 18 Remarks 18 Applications 19 Hyperspectral Image Classification 19 Multisensor Image Fusion 21 Conclusions 22
viii ļ Contents 3.1 3.2 3.2.1 3.2.2 3.2.3 3.3 3.3.1 3.3.2 3.3.3 3.4 3.4.1 3.4.2 3.5 24 Gonzalo Mateo-García, Valero Laparra, Christian Requena-Mesa, and Luis Gómez-Chova Introduction 24 Generative Adversarial Networks 25 Unsupervised GANs 25 Conditional GANs 26 Cycle-consistent GANs 27 GANs in Remote Sensing and Geosciences 28 GANs in Earth Observation 28 Conditional GANs in Earth Observation 30 CycleGANs in Earth Observation 30 Applications of GANs in Earth Observation 31 Domain Adaptation Across Satellites 31 Learning to Emulate Earth Systems from Observations 33 Conclusions and Perspectives 36 4 Deep Self-taught Learning in Remote Sensing 4.1 4.2 4.2.1 4.2.2 4.3 4.3.1 4.3.2 4.4 Ribana Roscher Introduction 37 Sparse Representation 38 Dictionary Learning 39 Self-taught Learning 40 Deep Self-taught Learning 40 Application Example 43 Relation to Deep Neural Networks Conclusion 45 3 5 Generative Adversarial Networks in the Geosciences 37 44 Deep Learning-based Semantic Segmentation in Remote Sensing 46 5.1 5.2 5.3 5.3.1 5.3.2 5.4 5.4.1 5.4.2 5.4.3 5.5 Devis Tuia, Diego Marcos, Konrad Schindler, and Bertrand Le Saux Introduction 46 Literature Review 47 Basics on Deep Semantic Segmentation: Computer Vision Models 49 Architectures for Image Data 49 Architectures for Point-clouds 52 Selected Examples 55 Encoding Invariances to Train Smaller Models: The example of Rotation Processing 3D Point Clouds as a Bundle of Images: SnapNet 59 Lake Ice Detection from Earth and from Space 62 Concluding Remarks 66 6 Object Detection in Remote Sensing 6.1 6.1.1 67 Jian Ding, Jinwang Wang, Wen
Yang, and Gui-Song Xia Introduction 67 Problem Description 67 55
Contents 6.1.2 6.1.3 6.1.4 6.1.4.1 6.1.4.2 6.1.5 6.2 6.2.1 6.2.1.1 6.2.1.2 6.2.2 6.2.2.1 6.2.2.2 6.3 6.3.1 6.3.1.1 6.3.1.2 6.3.1.3 6.3.1.4 6.3.2 6.3.2.1 6.3.2.2 6.3.2.3 6.3.2.4 6.3.3 6.3.3.1 6.3.3.2 6.4 6.4.1 6.4.2 6.4.3 6.5 Problem Settings of Object Detection 69 Object Representation in Remote Sensing 69 Evaluation Metrics 69 Precision-Recall Curve 70 Average Precision and Mean Average Precision 71 Applications 71 Preliminaries on Object Detection with Deep Models 72 Two-stage Algorithms 72 R-CNNs 72 R-FCN 73 One-stage Algorithms 73 YOLO 73 SSD 73 Object Detection in Optical RS Images 75 Related Works 75 Scale Variance 75 Orientation Variance 75 Oriented Object Detection 75 Detecting in Large-size Images 76 Datasets and Benchmark 77 DOTA 77 VisDrone 77 DIOR 77 xView 77 Two Representative Object Detectors in Optical RS Images Mask OBB 78 Rol Transformer 82 Object Detection in SAR Images 86 Challenges of Detection in SAR Images 86 Related Works 86 Datasets and Benchmarks 88 Conclusion 89 7 90 Benjamin Kettenberger, Onur Tasar, Bharath Bhushan Damodaran, Nicolas Courty, and Devis Tuia Introduction 90 Families of Methodologies 91 Selected Examples 93 Adapting the Inner Representation 93 Adapting the Inputs Distribution 97 Using (few, well chosen) Labels from the Target Domain 100 Concluding remarks 104 7.1 7.2 7.3 7.3.1 7.3.2 7.3.3 7.4 Deep Domain Adaptation in Earth Observation 78 ix
Contents 8 Recurrent Neural Networks and the Temporal Component 8.1 8.1.1 8.1.1.1 8.1.1.2 8.2 8.2.1 8.2.1.1 8.2.1.2 8.2.1.3 8.2.1.4 8.2.1.5 8.2.2 8.3 8.3.1 8.3.2 8.4 8.5 Marco Körner and Marc Rußwurm Recurrent Neural Networks 106 Training RNNs 107 Exploding and Vanishing Gradients 107 Circumventing Exploding and Vanishing Gradients 109 Gated Variants of RNNs 111 Long Short-term Memory Networks 111 The Cell State c( and the Hidden State ht 112 The Forget Gate ft 112 The Modulation Gate vt and the Input Gate it 112 The Output Gate ot 112 Training LSTM Networks 113 Other Gated Variants 113 Representative Capabilities of Recurrent Networks 114 Recurrent Neural Network Topologies 114 Experiments 115 Application in Earth Sciences 117 Conclusion 118 9 9.1 9.2 9.2.1 9.2.2 9.2.3 9.3 9.3.1 9.3.2 9.3.3 9.3.4 9.3.5 9.4 9.4.1 9.4.1.1 9.4.1.2 9.4.1.3 9.4.1.4 10 10.1 10.2 105 120 Maria Vakalopoulou, Stergios Christodoulidis, Mihir Sahasrabudhe, and Nikos Paragios Introduction 120 Literature Review 123 Classical Approaches 123 Deep Learning Techniques for Image Matching 124 Deep Learning Techniques for Image Registration 125 Image Registration with Deep Learning 126 2D Linear and Deformable Transformer 126 Network Architectures 127 Optimization Strategy 128 Dataset and Implementation Details 129 Experimental Results 129 Conclusion and Future Research 134 Challenges and Opportunities 134 Dataset with Annotations 134 Dimensionality of Data 135 Multi temporal Datasets 135 Robustness to Changed Areas 135 Deep Learning for Image Matching and Co-registration 136 Wei He, Danfeng Hong, Giuseppe
Scarpa, Tatsumi Uezato, and Naoto Yokoya Introduction 136 Pansharpening 137 Multisource Remote Sensing Image Fusion
Contents Survey of Pansharpening Methods Employing Deep Learning 137 10.2.1 Experimental Results 140 10.2.2 10.2.2.1 Experimental Design 140 10.2.2.2 Visual and Quantitative Comparison in Pansharpening 140 Multiband Image Fusion 143 10.3 Supervised Deep Learning-based Approaches 143 10.3.1 Unsupervised Deep Learning-based Approaches 145 10.3.2 Experimental Results 146 10.3.3 10.3.3.1 Comparison Methods and Evaluation Measures 146 10.3.3.2 Dataset and Experimental Setting 146 10.3.3.3 Quantitative Comparison and Visual Results 147 Conclusion and Outlook 148 10.4 11 11.1 11.2 11.3 11.4 Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives 150 Gencer Sumbul, Jian Kang, and Begüm Demir Introduction 150 Deep Learning for RS CBIR 152 Scalable RS CBIR Based on Deep Hashing 156 Discussion and Conclusion 159 Acknowledgement 160 Part II Making a Difference in the Geosciences With Deep Learning 161 163 Mayur Mudigonda, Prabhat Ram, Karthik Kashinath, Evan Racah, Ankur Mahesh, Yunjie Liu, Christopher Beckham, Jim Biard, Thorsten Kurth, Sookyung Kim, Samira Kahou, Tegan Maharaj, Burien Loring, Christopher Pal, Travis O’Brien, Ken Kunkel, Michael F. Wehner, and William D. Collins 12.1 Scientific Motivation 163 12.2 Tropical Cyclone and Atmospheric River Classification 166 12.2.1 Methods 166 12.2.2 Network Architecture 167 12.2.3 Results 169 12.3 Detection of Fronts 170 12.3.1 Analytical Approach 170 12.3.2 Dataset 171 12.3.3 Results 172 12.3.4 Limitations 174 12.4 Semi-supervised Classification and Localization of Extreme Events 175 12.4.1 Applications of Semi-
supervised Learning in Climate Modeling 175 12.4.1.1 Supervised Architecture 176 12.4.1.2 Semi-supervised Architecture 176 12.4.2 Results 176 12 Deep Learning for Detecting Extreme Weather Patterns xi
xii Contents 12.4.2.1 12.4.2.2 12.5 Frame-wise Reconstruction 176 Results and Discussion 178 Detecting Atmospheric Rivers and Tropical Cyclones Through Segmentation Methods 179 12.5.1 Modeling Approach 179 12.5.1.1 Segmentation Architecture 180 12.5.1.2 Climate Dataset and Labels 181 12.5.2 Architecture Innovations: Weighted Loss and Modified Network 181 12.5.3 Results 183 12.6 Challenges and Implications for the Future 184 12.7 Conclusions 185 13 Spatio-temporal Autoencoders in Weather and Climate Research 186 Xavier-Andoni Tibau, Christian Reimers, Christian Requena-Mesa, and Jakob Runge Introduction 186 13.1 13.2 Autoencoders 187 13.2.1 A Brief History of Autoencoders 188 Archetypes of Autoencoders 189 13.2.2 13.2.3 Variational Autoencoders (VAE) 191 13.2.4 Comparison Between Autoencoders and Classical Methods 192 13.3 Applications 193 13.3.1 Use of the Latent Space 193 13.3.1.1 Reduction of Dimensionality for the Understanding of the System Dynamics and its Interactions 195 13.3.1.2 Dimensionality Reduction for Feature Extraction and Prediction 199 13.3.2 Use of the Decoder 199 13.3.2.1 As a Random Sample Generator 201 13.3.2.2 Anomaly Detection 201 13.3.2.3 Use of a Denoising Autoencoder (DAE) Decoder 202 13.4 Conclusions and Outlook 203 14 14.1 14.2 14.3 14.4 14.5 Deep Learning to Improve Weather Predictions 204 Peter D. Dueben, Peter Bauer, and Samantha Adams Numerical Weather Prediction 204 How Will Machine Learning Enhance Weather Predictions? 207 Machine Learning Across the Workflow of Weather Prediction 208 Challenges for the Application of ML in Weather
Forecasts 213 The Way Forward 216 15 Deep Learning and the Weather Forecasting Problem: Precipitation 15.1 15.2 218 Zhihan Gao, Xingjian Shi, Hao Wang, Dit-Yan Yeung, Wang-chun Woo, and Wai-Kin Wong Introduction 218 Formulation 220 Nowcasting
Contents 15.3 15.4 15.4.1 15.4.2 15.4.3 15.4.4 15.4.5 15.4.6 15.4.7 15.4.8 15.5 15.5.1 15.5.2 15.5.3 15.5.4 15.6 Learning Strategies 221 Models 223 FNN-based Odels 223 RNN-based Models 225 Encoder-forecaster Structure 226 Convolutional LSTM 226 ConvLSTM with Star-shaped Bridge Predictive RNN 228 Memory in Memory Network 229 Trajectory GRU 231 Benchmark 233 HKO-7 Dataset 234 Evaluation Methodology 234 Evaluated Algorithms 235 Evaluation Results 236 Discussion 236 Appendix 238 Acknowledgement 239 16 Deep Learning for High-dimensional Parameter Retrieval 240 David Malmgren-Hansen Introduction 240 Deep Learning Parameter Retrieval Literature 242 Land 242 Ocean 243 Cryosphere 244 Global Weather Models 244 The Challenge of High-dimensional Problems 244 Computational Load of CNNs 247 Mean Square Error or Cross-entropy Optimization? 249 Applications and Examples 250 Utilizing High-Dimensional Spatio-spectral Information with CNNs 250 The Effect of Loss Functions in Retrieval of Sea Ice Concentrations 253 Conclusion 257 16.1 16.2 16.2.1 16.2.2 16.2.3 16.2.4 16.3 16.3.1 16.3.2 16.4 16.4.1 16.4.2 16.5 17 17.1 17.2 17.2.1 17.2.2 17.2.3 17.2.4 17.2.5 17.2.6 227 к Review of Deep Learning for Cryospheric Studies 258 Lin Liu Introduction 258 Deep-learning-based Remote Sensing Studies of the Cryosphere 260 Glaciers 260 Ice Sheet 261 Snow 262 Permafrost 263 Sea Ice 264 River Ice 265 xiii
xiv Contents 17.3 17.4 18 18.1 18.2 18.2.1 18.2.2 18.3 18.3.1 18.3.2 18.3.3 18.4 18.4.1 18.4.2 18.5 Deep-learning-based Modeling of the Cryosphere Summary and Prospect 266 Appendix: List of Data and Codes 267 265 269 Emulating Ecological Memory with Recurrent Neural Networks Basil Kraft, Simon Besnard, and Sajan Koirala Ecological Memory Effects: Concepts and Relevance 269 Data-driven Approaches for Ecological memory Effects 270 A Brief Overview of Memory Effects 270 Data-driven Methods for Memory Effects 271 Case Study: Emulating a Physical Model Using Recurrent Neural Networks 272 Physical Model Simulation Data 272 Experimental Design 273 RNN Setup and Training 274 Results and Discussion 276 The Predictive Capability Across Scales 276 Prediction of Seasonal Dynamics 279 Conclusions 281 Part III Linking Physics and Deep Learning Models 283 19.2.5 19.3 285 Chaopeng Shen and Kathryn Lawson Introduction 285 Deep Learning Applications in Hydrology 286 Dynamical System Modeling 286 Large-scale Hydrologic Modeling with Big Data 286 Data-limited LSTM Applications 290 Physics-constrained Hydrologic Machine Learning 292 Information Retrieval for Hydrology 293 Physically-informed Machine Learning for Subsurface Flow and Reactive Transport Modeling 294 Additional Observations 296 Current Limitations and Outlook 296 20 Deep Learning of Unresolved Turbulent Ocean Processes in Climate 20.1 20.2 20.3 20.3.1 20.3.2 Models 298 Laure Zanna and Thomas Bolton Introduction 298 The Parameterization Problem 299 Deep Learning Parameterizations of Subgrid Ocean Processes Why DL for Subgrid
Parameterizations? 300 Recent Advances in DL for Subgrid Parameterizations 300 19 19.1 19.2 19.2.1 19.2.1.1 19.2.1.2 19.2.2 19.2.3 19.2.4 Applications of Deep Learning in Hydrology 300
Contents 20.4 20.5 Physics-aware Deep Learning 301 Further Challenges ahead for Deep Learning Parameterizations 21 Deep Learning for the Parametrization of Subgrid Processes in Climate Models 307 21.3 21.4 Pierre Gentine, Veronika Eyring, and Tom Beucler Introduction 307 Deep Neural Networks for Moist Convection (Deep Clouds) Parametrization 309 Physical Constraints and Generalization 312 Future Challenges 314 22 Using Deep Learning to Correct Theoretically-derived Models 22.1 22.1.1 22.1.1.1 22.1.1.2 22.1.2 22.1.2.1 22.1.2.2 22.1.3 22.2 22.2.1 22.2.2 22.3 Peter A. G. Watson Experiments with the Lorenz ’96 System 317 The Lorenz’96 Equations and Coarse-scale Models Theoretically-derived Coarse-scale Model 318 Models with ANNs 319 Results 320 Single-timestep Tendency Prediction Errors 320 Forecast and Climate Prediction Skill 321 Testing Seamless Prediction 324 Discussion and Outlook 324 Towards Earth System Modeling 325 Application to Climate Change Studies 326 Conclusion 327 23 Outlook 21.1 21.2 303 315 318 328 Markus Reichstein, Gustau Camps-Valls, Devis Tuia, and Xiao Xiang Zhu Bibliography Index 401 331 XV
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| adam_txt |
Contents Foreword xvi Acknowledgments xvii List of Contributors xviii List of Acronyms xxiv 1 1.1 1.2 1.3 1.4 1 Gustau Camps-Valls, Xiao Xiang Zhu, Devis Tuia, and Markus Reichstein A Taxonomy of Deep Learning Approaches 2 Deep Learning in Remote Sensing 3 Deep Learning in Geosciences and Climate 7 Book Structure and Roadmap 9 Introduction Part I 2 2.1 2.2 2.2.1 2.2.2 2.2.3 2.3 2.3.1 2.3.2 2.4 Deep Learning to Extract Information from Remote Sensing Images 13 Learning Unsupervised Feature Representations of Remote Sensing Data with Sparse Convolutional Networks 15 Jose E. Adsuara, Manuel Campos-Taberner, Javier Garcia-Haro, Carlo Gatta, Adriana Romero, and Gustau Camps-Valls Introduction 15 Sparse Unsupervised Convolutional Networks 17 Sparsity as the Guiding Criterion 17 The EPLS Algorithm 18 Remarks 18 Applications 19 Hyperspectral Image Classification 19 Multisensor Image Fusion 21 Conclusions 22
viii ļ Contents 3.1 3.2 3.2.1 3.2.2 3.2.3 3.3 3.3.1 3.3.2 3.3.3 3.4 3.4.1 3.4.2 3.5 24 Gonzalo Mateo-García, Valero Laparra, Christian Requena-Mesa, and Luis Gómez-Chova Introduction 24 Generative Adversarial Networks 25 Unsupervised GANs 25 Conditional GANs 26 Cycle-consistent GANs 27 GANs in Remote Sensing and Geosciences 28 GANs in Earth Observation 28 Conditional GANs in Earth Observation 30 CycleGANs in Earth Observation 30 Applications of GANs in Earth Observation 31 Domain Adaptation Across Satellites 31 Learning to Emulate Earth Systems from Observations 33 Conclusions and Perspectives 36 4 Deep Self-taught Learning in Remote Sensing 4.1 4.2 4.2.1 4.2.2 4.3 4.3.1 4.3.2 4.4 Ribana Roscher Introduction 37 Sparse Representation 38 Dictionary Learning 39 Self-taught Learning 40 Deep Self-taught Learning 40 Application Example 43 Relation to Deep Neural Networks Conclusion 45 3 5 Generative Adversarial Networks in the Geosciences 37 44 Deep Learning-based Semantic Segmentation in Remote Sensing 46 5.1 5.2 5.3 5.3.1 5.3.2 5.4 5.4.1 5.4.2 5.4.3 5.5 Devis Tuia, Diego Marcos, Konrad Schindler, and Bertrand Le Saux Introduction 46 Literature Review 47 Basics on Deep Semantic Segmentation: Computer Vision Models 49 Architectures for Image Data 49 Architectures for Point-clouds 52 Selected Examples 55 Encoding Invariances to Train Smaller Models: The example of Rotation Processing 3D Point Clouds as a Bundle of Images: SnapNet 59 Lake Ice Detection from Earth and from Space 62 Concluding Remarks 66 6 Object Detection in Remote Sensing 6.1 6.1.1 67 Jian Ding, Jinwang Wang, Wen
Yang, and Gui-Song Xia Introduction 67 Problem Description 67 55
Contents 6.1.2 6.1.3 6.1.4 6.1.4.1 6.1.4.2 6.1.5 6.2 6.2.1 6.2.1.1 6.2.1.2 6.2.2 6.2.2.1 6.2.2.2 6.3 6.3.1 6.3.1.1 6.3.1.2 6.3.1.3 6.3.1.4 6.3.2 6.3.2.1 6.3.2.2 6.3.2.3 6.3.2.4 6.3.3 6.3.3.1 6.3.3.2 6.4 6.4.1 6.4.2 6.4.3 6.5 Problem Settings of Object Detection 69 Object Representation in Remote Sensing 69 Evaluation Metrics 69 Precision-Recall Curve 70 Average Precision and Mean Average Precision 71 Applications 71 Preliminaries on Object Detection with Deep Models 72 Two-stage Algorithms 72 R-CNNs 72 R-FCN 73 One-stage Algorithms 73 YOLO 73 SSD 73 Object Detection in Optical RS Images 75 Related Works 75 Scale Variance 75 Orientation Variance 75 Oriented Object Detection 75 Detecting in Large-size Images 76 Datasets and Benchmark 77 DOTA 77 VisDrone 77 DIOR 77 xView 77 Two Representative Object Detectors in Optical RS Images Mask OBB 78 Rol Transformer 82 Object Detection in SAR Images 86 Challenges of Detection in SAR Images 86 Related Works 86 Datasets and Benchmarks 88 Conclusion 89 7 90 Benjamin Kettenberger, Onur Tasar, Bharath Bhushan Damodaran, Nicolas Courty, and Devis Tuia Introduction 90 Families of Methodologies 91 Selected Examples 93 Adapting the Inner Representation 93 Adapting the Inputs Distribution 97 Using (few, well chosen) Labels from the Target Domain 100 Concluding remarks 104 7.1 7.2 7.3 7.3.1 7.3.2 7.3.3 7.4 Deep Domain Adaptation in Earth Observation 78 ix
Contents 8 Recurrent Neural Networks and the Temporal Component 8.1 8.1.1 8.1.1.1 8.1.1.2 8.2 8.2.1 8.2.1.1 8.2.1.2 8.2.1.3 8.2.1.4 8.2.1.5 8.2.2 8.3 8.3.1 8.3.2 8.4 8.5 Marco Körner and Marc Rußwurm Recurrent Neural Networks 106 Training RNNs 107 Exploding and Vanishing Gradients 107 Circumventing Exploding and Vanishing Gradients 109 Gated Variants of RNNs 111 Long Short-term Memory Networks 111 The Cell State c( and the Hidden State ht 112 The Forget Gate ft 112 The Modulation Gate vt and the Input Gate it 112 The Output Gate ot 112 Training LSTM Networks 113 Other Gated Variants 113 Representative Capabilities of Recurrent Networks 114 Recurrent Neural Network Topologies 114 Experiments 115 Application in Earth Sciences 117 Conclusion 118 9 9.1 9.2 9.2.1 9.2.2 9.2.3 9.3 9.3.1 9.3.2 9.3.3 9.3.4 9.3.5 9.4 9.4.1 9.4.1.1 9.4.1.2 9.4.1.3 9.4.1.4 10 10.1 10.2 105 120 Maria Vakalopoulou, Stergios Christodoulidis, Mihir Sahasrabudhe, and Nikos Paragios Introduction 120 Literature Review 123 Classical Approaches 123 Deep Learning Techniques for Image Matching 124 Deep Learning Techniques for Image Registration 125 Image Registration with Deep Learning 126 2D Linear and Deformable Transformer 126 Network Architectures 127 Optimization Strategy 128 Dataset and Implementation Details 129 Experimental Results 129 Conclusion and Future Research 134 Challenges and Opportunities 134 Dataset with Annotations 134 Dimensionality of Data 135 Multi temporal Datasets 135 Robustness to Changed Areas 135 Deep Learning for Image Matching and Co-registration 136 Wei He, Danfeng Hong, Giuseppe
Scarpa, Tatsumi Uezato, and Naoto Yokoya Introduction 136 Pansharpening 137 Multisource Remote Sensing Image Fusion
Contents Survey of Pansharpening Methods Employing Deep Learning 137 10.2.1 Experimental Results 140 10.2.2 10.2.2.1 Experimental Design 140 10.2.2.2 Visual and Quantitative Comparison in Pansharpening 140 Multiband Image Fusion 143 10.3 Supervised Deep Learning-based Approaches 143 10.3.1 Unsupervised Deep Learning-based Approaches 145 10.3.2 Experimental Results 146 10.3.3 10.3.3.1 Comparison Methods and Evaluation Measures 146 10.3.3.2 Dataset and Experimental Setting 146 10.3.3.3 Quantitative Comparison and Visual Results 147 Conclusion and Outlook 148 10.4 11 11.1 11.2 11.3 11.4 Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives 150 Gencer Sumbul, Jian Kang, and Begüm Demir Introduction 150 Deep Learning for RS CBIR 152 Scalable RS CBIR Based on Deep Hashing 156 Discussion and Conclusion 159 Acknowledgement 160 Part II Making a Difference in the Geosciences With Deep Learning 161 163 Mayur Mudigonda, Prabhat Ram, Karthik Kashinath, Evan Racah, Ankur Mahesh, Yunjie Liu, Christopher Beckham, Jim Biard, Thorsten Kurth, Sookyung Kim, Samira Kahou, Tegan Maharaj, Burien Loring, Christopher Pal, Travis O’Brien, Ken Kunkel, Michael F. Wehner, and William D. Collins 12.1 Scientific Motivation 163 12.2 Tropical Cyclone and Atmospheric River Classification 166 12.2.1 Methods 166 12.2.2 Network Architecture 167 12.2.3 Results 169 12.3 Detection of Fronts 170 12.3.1 Analytical Approach 170 12.3.2 Dataset 171 12.3.3 Results 172 12.3.4 Limitations 174 12.4 Semi-supervised Classification and Localization of Extreme Events 175 12.4.1 Applications of Semi-
supervised Learning in Climate Modeling 175 12.4.1.1 Supervised Architecture 176 12.4.1.2 Semi-supervised Architecture 176 12.4.2 Results 176 12 Deep Learning for Detecting Extreme Weather Patterns xi
xii Contents 12.4.2.1 12.4.2.2 12.5 Frame-wise Reconstruction 176 Results and Discussion 178 Detecting Atmospheric Rivers and Tropical Cyclones Through Segmentation Methods 179 12.5.1 Modeling Approach 179 12.5.1.1 Segmentation Architecture 180 12.5.1.2 Climate Dataset and Labels 181 12.5.2 Architecture Innovations: Weighted Loss and Modified Network 181 12.5.3 Results 183 12.6 Challenges and Implications for the Future 184 12.7 Conclusions 185 13 Spatio-temporal Autoencoders in Weather and Climate Research 186 Xavier-Andoni Tibau, Christian Reimers, Christian Requena-Mesa, and Jakob Runge Introduction 186 13.1 13.2 Autoencoders 187 13.2.1 A Brief History of Autoencoders 188 Archetypes of Autoencoders 189 13.2.2 13.2.3 Variational Autoencoders (VAE) 191 13.2.4 Comparison Between Autoencoders and Classical Methods 192 13.3 Applications 193 13.3.1 Use of the Latent Space 193 13.3.1.1 Reduction of Dimensionality for the Understanding of the System Dynamics and its Interactions 195 13.3.1.2 Dimensionality Reduction for Feature Extraction and Prediction 199 13.3.2 Use of the Decoder 199 13.3.2.1 As a Random Sample Generator 201 13.3.2.2 Anomaly Detection 201 13.3.2.3 Use of a Denoising Autoencoder (DAE) Decoder 202 13.4 Conclusions and Outlook 203 14 14.1 14.2 14.3 14.4 14.5 Deep Learning to Improve Weather Predictions 204 Peter D. Dueben, Peter Bauer, and Samantha Adams Numerical Weather Prediction 204 How Will Machine Learning Enhance Weather Predictions? 207 Machine Learning Across the Workflow of Weather Prediction 208 Challenges for the Application of ML in Weather
Forecasts 213 The Way Forward 216 15 Deep Learning and the Weather Forecasting Problem: Precipitation 15.1 15.2 218 Zhihan Gao, Xingjian Shi, Hao Wang, Dit-Yan Yeung, Wang-chun Woo, and Wai-Kin Wong Introduction 218 Formulation 220 Nowcasting
Contents 15.3 15.4 15.4.1 15.4.2 15.4.3 15.4.4 15.4.5 15.4.6 15.4.7 15.4.8 15.5 15.5.1 15.5.2 15.5.3 15.5.4 15.6 Learning Strategies 221 Models 223 FNN-based Odels 223 RNN-based Models 225 Encoder-forecaster Structure 226 Convolutional LSTM 226 ConvLSTM with Star-shaped Bridge Predictive RNN 228 Memory in Memory Network 229 Trajectory GRU 231 Benchmark 233 HKO-7 Dataset 234 Evaluation Methodology 234 Evaluated Algorithms 235 Evaluation Results 236 Discussion 236 Appendix 238 Acknowledgement 239 16 Deep Learning for High-dimensional Parameter Retrieval 240 David Malmgren-Hansen Introduction 240 Deep Learning Parameter Retrieval Literature 242 Land 242 Ocean 243 Cryosphere 244 Global Weather Models 244 The Challenge of High-dimensional Problems 244 Computational Load of CNNs 247 Mean Square Error or Cross-entropy Optimization? 249 Applications and Examples 250 Utilizing High-Dimensional Spatio-spectral Information with CNNs 250 The Effect of Loss Functions in Retrieval of Sea Ice Concentrations 253 Conclusion 257 16.1 16.2 16.2.1 16.2.2 16.2.3 16.2.4 16.3 16.3.1 16.3.2 16.4 16.4.1 16.4.2 16.5 17 17.1 17.2 17.2.1 17.2.2 17.2.3 17.2.4 17.2.5 17.2.6 227 к Review of Deep Learning for Cryospheric Studies 258 Lin Liu Introduction 258 Deep-learning-based Remote Sensing Studies of the Cryosphere 260 Glaciers 260 Ice Sheet 261 Snow 262 Permafrost 263 Sea Ice 264 River Ice 265 xiii
xiv Contents 17.3 17.4 18 18.1 18.2 18.2.1 18.2.2 18.3 18.3.1 18.3.2 18.3.3 18.4 18.4.1 18.4.2 18.5 Deep-learning-based Modeling of the Cryosphere Summary and Prospect 266 Appendix: List of Data and Codes 267 265 269 Emulating Ecological Memory with Recurrent Neural Networks Basil Kraft, Simon Besnard, and Sajan Koirala Ecological Memory Effects: Concepts and Relevance 269 Data-driven Approaches for Ecological memory Effects 270 A Brief Overview of Memory Effects 270 Data-driven Methods for Memory Effects 271 Case Study: Emulating a Physical Model Using Recurrent Neural Networks 272 Physical Model Simulation Data 272 Experimental Design 273 RNN Setup and Training 274 Results and Discussion 276 The Predictive Capability Across Scales 276 Prediction of Seasonal Dynamics 279 Conclusions 281 Part III Linking Physics and Deep Learning Models 283 19.2.5 19.3 285 Chaopeng Shen and Kathryn Lawson Introduction 285 Deep Learning Applications in Hydrology 286 Dynamical System Modeling 286 Large-scale Hydrologic Modeling with Big Data 286 Data-limited LSTM Applications 290 Physics-constrained Hydrologic Machine Learning 292 Information Retrieval for Hydrology 293 Physically-informed Machine Learning for Subsurface Flow and Reactive Transport Modeling 294 Additional Observations 296 Current Limitations and Outlook 296 20 Deep Learning of Unresolved Turbulent Ocean Processes in Climate 20.1 20.2 20.3 20.3.1 20.3.2 Models 298 Laure Zanna and Thomas Bolton Introduction 298 The Parameterization Problem 299 Deep Learning Parameterizations of Subgrid Ocean Processes Why DL for Subgrid
Parameterizations? 300 Recent Advances in DL for Subgrid Parameterizations 300 19 19.1 19.2 19.2.1 19.2.1.1 19.2.1.2 19.2.2 19.2.3 19.2.4 Applications of Deep Learning in Hydrology 300
Contents 20.4 20.5 Physics-aware Deep Learning 301 Further Challenges ahead for Deep Learning Parameterizations 21 Deep Learning for the Parametrization of Subgrid Processes in Climate Models 307 21.3 21.4 Pierre Gentine, Veronika Eyring, and Tom Beucler Introduction 307 Deep Neural Networks for Moist Convection (Deep Clouds) Parametrization 309 Physical Constraints and Generalization 312 Future Challenges 314 22 Using Deep Learning to Correct Theoretically-derived Models 22.1 22.1.1 22.1.1.1 22.1.1.2 22.1.2 22.1.2.1 22.1.2.2 22.1.3 22.2 22.2.1 22.2.2 22.3 Peter A. G. Watson Experiments with the Lorenz ’96 System 317 The Lorenz’96 Equations and Coarse-scale Models Theoretically-derived Coarse-scale Model 318 Models with ANNs 319 Results 320 Single-timestep Tendency Prediction Errors 320 Forecast and Climate Prediction Skill 321 Testing Seamless Prediction 324 Discussion and Outlook 324 Towards Earth System Modeling 325 Application to Climate Change Studies 326 Conclusion 327 23 Outlook 21.1 21.2 303 315 318 328 Markus Reichstein, Gustau Camps-Valls, Devis Tuia, and Xiao Xiang Zhu Bibliography Index 401 331 XV |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author2 | Camps-Valls, Gustavo 1972- Tuia, Devis Zhu, Xiaoxiang 1984- Reichstein, Markus 1972- |
| author2_role | edt edt edt edt |
| author2_variant | g c v gcv d t dt x z xz m r mr |
| author_GND | (DE-588)140281436 (DE-588)1013459822 (DE-588)1247760588 |
| author_facet | Camps-Valls, Gustavo 1972- Tuia, Devis Zhu, Xiaoxiang 1984- Reichstein, Markus 1972- |
| building | Verbundindex |
| bvnumber | BV047474945 |
| callnumber-first | Q - Science |
| callnumber-label | QE26 |
| callnumber-raw | QE26.3 |
| callnumber-search | QE26.3 |
| callnumber-sort | QE 226.3 |
| callnumber-subject | QE - Geology |
| classification_rvk | RB 10232 RB 10115 RB 10104 RB 10438 |
| ctrlnum | (OCoLC)1256406696 (DE-599)KXP1752287746 |
| dewey-full | 550.71 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 550 - Earth sciences |
| dewey-raw | 550.71 |
| dewey-search | 550.71 |
| dewey-sort | 3550.71 |
| dewey-tens | 550 - Earth sciences |
| discipline | Geologie / Paläontologie Geographie |
| discipline_str_mv | Geologie / Paläontologie Geographie |
| format | Book |
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| spelling | Deep learning for the earth sciences a comprehensive approach to remote sensing, climate science and geosciences edited by Gustau Camps-Valls (Universitat de València, Spain), Devis Tuia (EPFL, Switzerland), Xiao Xiang Zhu (German Aerospace Center and Technical University of Munich, Germany), Markus Reichstein (Max Planck Institute, Germany) Hoboken, NJ Wiley [2021] xxxvi, 405 Seiten Illustrationen, Diagramme, Karten (schwarz-weiß) txt rdacontent n rdamedia nc rdacarrier 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. Fernerkundung (DE-588)4016796-3 gnd rswk-swf Geowissenschaften (DE-588)4020288-4 gnd rswk-swf Klimatologie (DE-588)4031178-8 gnd rswk-swf Earth sciences / Study and teaching Algorithms / Study and teaching (DE-588)4143413-4 Aufsatzsammlung gnd-content Geowissenschaften (DE-588)4020288-4 s Fernerkundung (DE-588)4016796-3 s Klimatologie (DE-588)4031178-8 s DE-604 Camps-Valls, Gustavo 1972- (DE-588)140281436 edt Tuia, Devis edt Zhu, Xiaoxiang 1984- (DE-588)1013459822 edt Reichstein, Markus 1972- (DE-588)1247760588 edt Erscheint auch als Online-Ausgabe, EPUB 978-1-119-64616-7 Erscheint auch als Online-Ausgabe, PDF 978-1-119-64615-0 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032876542&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Deep learning for the earth sciences a comprehensive approach to remote sensing, climate science and geosciences Fernerkundung (DE-588)4016796-3 gnd Geowissenschaften (DE-588)4020288-4 gnd Klimatologie (DE-588)4031178-8 gnd |
| subject_GND | (DE-588)4016796-3 (DE-588)4020288-4 (DE-588)4031178-8 (DE-588)4143413-4 |
| title | Deep learning for the earth sciences a comprehensive approach to remote sensing, climate science and geosciences |
| title_auth | Deep learning for the earth sciences a comprehensive approach to remote sensing, climate science and geosciences |
| title_exact_search | Deep learning for the earth sciences a comprehensive approach to remote sensing, climate science and geosciences |
| title_exact_search_txtP | Deep learning for the earth sciences a comprehensive approach to remote sensing, climate science and geosciences |
| title_full | Deep learning for the earth sciences a comprehensive approach to remote sensing, climate science and geosciences edited by Gustau Camps-Valls (Universitat de València, Spain), Devis Tuia (EPFL, Switzerland), Xiao Xiang Zhu (German Aerospace Center and Technical University of Munich, Germany), Markus Reichstein (Max Planck Institute, Germany) |
| title_fullStr | Deep learning for the earth sciences a comprehensive approach to remote sensing, climate science and geosciences edited by Gustau Camps-Valls (Universitat de València, Spain), Devis Tuia (EPFL, Switzerland), Xiao Xiang Zhu (German Aerospace Center and Technical University of Munich, Germany), Markus Reichstein (Max Planck Institute, Germany) |
| title_full_unstemmed | Deep learning for the earth sciences a comprehensive approach to remote sensing, climate science and geosciences edited by Gustau Camps-Valls (Universitat de València, Spain), Devis Tuia (EPFL, Switzerland), Xiao Xiang Zhu (German Aerospace Center and Technical University of Munich, Germany), Markus Reichstein (Max Planck Institute, Germany) |
| title_short | Deep learning for the earth sciences |
| title_sort | deep learning for the earth sciences a comprehensive approach to remote sensing climate science and geosciences |
| title_sub | a comprehensive approach to remote sensing, climate science and geosciences |
| topic | Fernerkundung (DE-588)4016796-3 gnd Geowissenschaften (DE-588)4020288-4 gnd Klimatologie (DE-588)4031178-8 gnd |
| topic_facet | Fernerkundung Geowissenschaften Klimatologie Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032876542&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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