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Artificial Intelligence in Earth Science
Best Practices and Fundamental Challenges
- 1st Edition - April 26, 2023
- Editors: Ziheng Sun, Nicoleta Cristea, Pablo Rivas
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 7 3 7 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 7 2 1 6 - 1
Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges provides a comprehensive, step-by-step guide to AI workflows for solving problems in Earth… Read more
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Request a sales quoteArtificial Intelligence in Earth Science: Best Practices and Fundamental Challenges provides a comprehensive, step-by-step guide to AI workflows for solving problems in Earth Science. The book focuses on the most challenging problems in applying AI in Earth system sciences, such as training data preparation, model selection, hyperparameter tuning, model structure optimization, spatiotemporal generalization, transforming model results into products, and explaining trained models. In addition, it provides full-stack workflow tutorials to help walk readers through the whole process, regardless of previous AI experience.
The book tackles the complexity of Earth system problems in AI engineering, fully guiding geoscientists who are planning to implement AI in their daily work.
The book tackles the complexity of Earth system problems in AI engineering, fully guiding geoscientists who are planning to implement AI in their daily work.
- Provides practical, step-by-step guides for Earth Scientists who are interested in implementing AI techniques in their work
- Features case studies to show real-world examples of techniques described in the book
- Includes additional elements to help readers who are new to AI, including end-of-chapter, key concept bulleted lists that concisely cover key concepts in the chapter
Researchers and professionals across Earth Science branches including geology, remote sensing, climate science, atmospheric science, agriculture, oceanography, etc.
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1: Introduction of artificial intelligence in Earth sciences
- Abstract
- 1: Background and motivation
- 2: AI evolution in Earth sciences
- 3: Latest developments and challenges
- 4: Short-term and long-term expectations for AI
- 5: Future developments and how to adapt
- 6: Practical AI: From prototype to operation
- 7: Why do we write this book?
- 8: Learning goals and tasks
- 9: Assignments & open questions
- References
- Chapter 2: Machine learning for snow cover mapping
- Abstract
- 1: Introduction
- 2: Machine learning tools and model
- 3: Data preparation
- 4: Model parameter tuning
- 5: Model training
- 6: Model performance evaluation
- 7: Conclusion
- 8: Assignment
- 9: Open questions
- References
- Chapter 3: AI for sea ice forecasting
- Abstract
- 1: Introduction
- 2: Sea ice seasonal forecast
- 3: Sea ice data exploration
- 4: ML approaches for sea ice forecasting
- 5: Results and analysis
- 6: Discussion
- 7: Open questions
- 8: Assignments
- References
- Chapter 4: Deep learning for ocean mesoscale eddy detection
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Chapter layout
- 3: Data preparation
- 4: Training and evaluating an eddy detection model
- 5: Discussion
- 6: Summary
- 7: Assignments
- 8: Open questions
- References
- Chapter 5: Artificial intelligence for plant disease recognition
- Abstract
- 1: Introduction
- 2: Data retrieval and preparation
- 3: Step-by-step implementation
- 4: Experimental results and how to select a model
- 5: Discussion
- 6: Conclusion
- 7: Assignment
- 8: Open questions
- References
- Chapter 6: Spatiotemporal attention ConvLSTM networks for predicting and physically interpreting wildfire spread
- Abstract
- 1: Introduction
- 2: Methodology
- 3: Earth AI workflow
- 4: Results
- 5: Conclusions
- 6: Assignment
- 7: Open questions
- References
- Chapter 7: AI for physics-inspired hydrology modeling
- Abstract
- 1: Introduction and background
- 2: PyTorch and autodifferentiation
- 3: Extremely brief background on numerical optimization
- 4: Bringing things together: Solving ODEs inside of neural networks
- 5: Scaling up to a conceptual hydrologic model
- 6: Conclusions
- References
- Further reading
- Chapter 8: Theory of spatiotemporal deep analogs and their application to solar forecasting
- Abstract
- 1: Introduction
- 2: Research data
- 3: Methodology
- 4: Results and discussion
- 5: Final remarks
- 6: Assignment
- 7: Open questions
- Appendix A: Deep learning layers and operators
- Appendix B: Verification of extended analog search with GFS
- Appendix C: Weather analog identification under a high irradiance regime
- Appendix D: Model attribution
- References
- Chapter 9: AI for improving ozone forecasting
- Abstract
- 1: Introduction
- 2: Background
- 3: Data collection
- 4: Dataset preparation
- 5: Machine learning
- 6: ML workflow management
- 7: Discussion
- 8: Conclusion
- 9: Assignment
- 10: Open questions
- 11: Lessons learned
- References
- Chapter 10: AI for monitoring power plant emissions from space
- Abstract
- 1: Introduction
- 2: Background
- 3: Data collection
- 4: Preprocessing
- 5: Machine learning
- 6: Managing emission AI workflow in Geoweaver
- 7: Discussion
- 8: Summary
- 9: Assignment
- 10: Open questions
- 11: Lessons learned
- References
- Chapter 11: AI for shrubland identification and mapping
- Abstract
- 1: Introduction
- 2: What you’ll learn
- 3: Background
- 4: Prerequisites
- 5: Model building
- 6: Discussion
- 7: Summary
- 8: Assignment
- 9: Open questions
- References
- Chapter 12: Explainable AI for understanding ML-derived vegetation products
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Background
- 3: Prerequisites
- 4: Method & technique
- 5: Experiment & results
- 6: Summary
- 7: Assignment
- 8: Open questions
- 9: Lessons learned
- References
- Further reading
- Chapter 13: Satellite image classification using quantum machine learning
- Abstract
- Acknowledgment
- 1: Introduction
- 2: Data
- 3: Applying QML on MODIS hyperspectral images
- 4: Conclusions
- 5: Assignments
- 6: Open questions
- References
- Chapter 14: Provenance in earth AI
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Overview of relevant concepts in provenance, XAI, and TAI
- 3: Need for provenance in earth AI
- 4: Technical approaches
- 5: Discussion
- 6: Conclusions
- 7: Assignment
- 8: Open questions
- References
- Chapter 15: AI ethics for earth sciences
- Abstract
- 1: Introduction
- 2: Prior work
- 3: Addressing ethical concerns during system design
- 4: Considerating algorithmic bias
- 5: Designing ethically driven automated systems
- 6: Assessing the impact of autonomous and intelligent systems on human well-being
- 7: Developing AI literacy, skills, and readiness
- 8: On documenting datasets for AI
- 9: On documenting AI models
- 10: Carbon emissions of earth AI models
- 11: Conclusions
- 12: Assignments
- 13: Open questions
- References
- Index
- No. of pages: 430
- Language: English
- Edition: 1
- Published: April 26, 2023
- Imprint: Elsevier
- Paperback ISBN: 9780323917377
- eBook ISBN: 9780323972161
ZS
Ziheng Sun
Ziheng Sun is a Principal Investigator at the Center for Spatial Information Science and Systems, and a research assistant professor the Department of Geography and Geoinformation Science at George Mason University. He is a practitioner of using the latest technologies such as artificial intelligence and high-performance computing, to seek for answers to the questions in geoscience. He invented RSSI, a novel index for artificial object recognition from high resolution aerial images, and proposed parameterless automatic classification solution for reducing the parameter-tuning burden on scientists. Prof Sun has published over 50 papers in renowned journals in geoscience and has worked on several federal-funded projects to build geospatial cyberinfrastructure systems for better disseminating, processing, visualizing, and understanding spatial big data.
Affiliations and expertise
Principal Investigator, Center for Spatial Information Science and Systems, George Mason University, USANC
Nicoleta Cristea
Nicoleta Cristea is a research scientist in the Department of Civil and Environmental Engineering at the University of Washington (UW), a research scientist with the UW Freshwater Initiative, and a data science fellow at the UW eScience Institute. Her current research focus is on modeling snow surface temperature and evaluating spatially distributed hydrologic models. Nicoleta is currently leading an NSF-funded project on mapping snow covered areas from Cubesat imagery using deep learning techniques.
Affiliations and expertise
Research scientist, Department of Civil and Environmental Engineering, University of Washington, USAPR
Pablo Rivas
Pablo Rivas is assistant professor of computer science at Baylor University where he teaches courses related to machine learning, deep learning, data mining, and theory. His research areas include deep machine learning and large-scale data mining in big data analytics, large-scale multidimensional multispectral signal analysis, statistical pattern recognition methods, image restoration, image analysis, intelligent software systems, and health-care imaging. Other research areas include applied mathematics, numerical optimization, swarm intelligence optimization, evolutionary algorithms, soft computing, fuzzy logic, neural networks, and neuro-fuzzy systems.
Affiliations and expertise
Assistant Professor of Computer Science, Baylor University, USARead Artificial Intelligence in Earth Science on ScienceDirect