Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges

by Ziheng Sun (Editor), Nicoleta Cristea (Editor), Pablo Rivas (Editor)

Overview

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 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. - 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

Product Details

ISBN-13:	9780323972161
Publisher:	Elsevier Science
Publication date:	04/27/2023
Sold by:	Barnes & Noble
Format:	eBook
Pages:	430
File size:	89 MB
Note:	This product may take a few minutes to download.

About the Author

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.
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.
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.

Table of Contents

1. Introduction of artificial intelligence in Earth sciences 2. Machine learning for snow cover mapping 3. AI for sea ice forecasting 4. Deep learning for ocean mesoscale eddy detection 5. Artificial intelligence for plant disease recognition 6. Spatiotemporal attention ConvLSTM networks for predicting and physically interpreting wildfire spread 7. AI for physics-inspired hydrology modeling 8. Theory of spatiotemporal deep analogs and their application to solar forecasting 9. AI for improving ozone forecasting 10. AI for monitoring power plant emissions from space 11. AI for shrubland identification and mapping 12. Explainable AI for understanding ML-derived vegetation products 13. Satellite image classification using quantum machine learning 14. Provenance in earth AI 15. AI ethics for earth sciences

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From the Publisher

A detailed, step-by-step guide to implementing AI techniques in Earth Science to discover patterns, trends, and answers that human analysis cannot match