Artificial Intelligence in Earth Science

Best Practices and Fundamental Challenges
Erscheinungsjahr 2023
ISBN: 978-0-323-91737-7
Verlag: Elsevier - Health Sciences Division

Buch, Englisch, 430 Seiten, Format (B × H): 235 mm x 190 mm, Gewicht: 1000 g

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.

Zielgruppe

Researchers and professionals across Earth Science branches including geology, remote sensing, climate science, atmospheric science, agriculture, oceanography, etc.

Autoren/Hrsg.

Cristea, Nicoleta

Sun, Ziheng

Rivas, Pablo

Fachgebiete

• Geowissenschaften Geologie Geologie

Inhaltsverzeichnis

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

Über Autor(innen)

Sun, Ziheng
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.

Rivas, Pablo
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.

Cristea, Nicoleta
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.