Note: 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.