The repo contains the source code, notebooks, and technical resources that assist students to read the book Artificial Intelligence in Earth Science.

Authors: Ziheng Sun, Nicoleta Cristea, Kehan Yang, Aji John, Sahara Ali, Yiyi Huang, Jianwu Wang, Edwin Goh, Annie Didier, Jinbo Wang, Jayme Garcia Arnal Barbedo, Arif Masrur, Manzhu Yu, Andrew Bennett, Weiming Hu, Guido Cervone, George Young, Ahmed Alnuaim (Alnaim), Didarul Islam, Michael J. Mahoney, Lucas K. Johnson, Colin M. Beier, Geetha Satya Mounika Ganji, Wai Hang Chow Lin, Olawale Ayoade, Pablo Rivas, Nurul Rafi, Amruta Kale, Xiaogang Ma, Christopher Thompson, Gissella Bejarano

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.

Explore the exciting intersection of artificial intelligence and Earth sciences, uncovering the transformative potential of AI in tackling complex environmental challenges.

Discover how machine learning techniques can be leveraged to accurately map and monitor snow cover, facilitating better understanding of snow dynamics and their implications.

Dive into the world of AI-driven sea ice forecasting, uncovering advanced techniques for predicting sea ice behavior and enhancing navigational safety in polar regions.

Explore the applications of deep learning in detecting and characterizing mesoscale eddies in the ocean, unraveling their significance in global ocean circulation.

Delve into the realm of AI-based plant disease recognition, uncovering cutting-edge approaches for early detection and management of plant diseases to ensure crop health and productivity.

Discover how spatiotemporal attention ConvLSTM networks can revolutionize wildfire spread prediction, enabling proactive measures for fire management and prevention.

Explore the synergy of AI and physics in hydrology modeling, harnessing advanced techniques to simulate and understand complex hydrological processes for effective water resource management.

Unveil the theory of spatiotemporal deep analogs and their remarkable applications in solar forecasting, facilitating improved renewable energy integration and management.

Investigate the role of AI in enhancing ozone forecasting capabilities, enabling better understanding of air quality and supporting informed decision-making.

Explore how AI can be utilized to monitor and analyze power plant emissions from space, facilitating effective environmental management and mitigation strategies.

Uncover the potential of AI in identifying and mapping shrubland areas, providing valuable insights for ecological assessment and land management practices.

Gain insights into explainable AI techniques for understanding machine learning-derived vegetation products, enabling reliable interpretation and validation of remote sensing data.

Discover the emerging field of quantum machine learning applied to satellite image classification, unraveling the potential for improved accuracy and efficiency in remote sensing tasks.

Explore the significance of provenance in Earth AI, delving into techniques for ensuring transparency, reproducibility, and accountability in AI-driven Earth science workflows.

Dive into the ethical dimensions of AI in Earth sciences, examining the challenges, implications, and guidelines for responsible and ethical use of AI technologies.

Click on the chapter titles to access the respective chapters and start an enlightening journey exploring the diverse applications of AI in Earth sciences!