Books in Computers in geosciences

1-10 of 28 results in All results

Deep Learning for Earth Observation and Climate Monitoring

• 1st Edition
• March 1, 2025
• Uzair Aslam Bhatti + 3 more
• English
• Paperback 978-0-443-24712-5

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• eBook 978-0-443-24713-2

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Deep Learning for Earth Observation and Climate Monitoring bridges the gap between deep learning and the Earth sciences, offering cutting-edge techniques and applications that are transforming our understanding of the environment. With a focus on practical scenarios, this book introduces readers to the fundamental concepts of deep learning, from classification and image segmentation to anomaly detection and domain adaptability. The book includes practical discussion on regression, parameter retrieval, forecasting, and interpolation, among other topics. With a solid foundational theory, real-world examples, and example codes, it provides a full understanding of how intelligent systems can be applied to enhance Earth observation and especially climate monitoring.This book allows readers to apply learning representations, unsupervised deep learning, and physics-aware models to Earth observation data, enabling them to leverage the power of deep learning to fully utilize the wealth of environmental data from satellite technologies.

Computational Methods for Time-Series Analysis in Earth Sciences

• 1st Edition
• March 1, 2025
• Silvio José Gumiere + 1 more
• English
• Paperback 978-0-443-33631-7

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• eBook 978-0-443-33632-4

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Computational Methods for Time-Series Analysis in Earth Sciences bridges the gap between theoretical knowledge and practical application, offering a deep dive into the utilization of R programming for managing, analyzing, and forecasting time-series data within the Earth sciences. The book systematically unfolds the layers of data manipulation, graphical representation, and sampling to prepare the reader for complex analyses and predictive modeling, from the basics of signal processing to the nuances of machine learning. It presents cutting-edge techniques, such as neural networks, kernel-based methods, and evolutionary algorithms, specifically tailored to tackle challenges, and provides practical case studies to aid readers.This is a valuable resource for scientists, researchers, and students delving into the intricacies of Earth's environmental patterns and cycles through the lens of computational analysis. It guides readers through various computational approaches for deciphering spatial and temporal data.

Deep Learning for Multi-Sensor Earth Observation

• 1st Edition
• February 1, 2025
• Sudipan Saha
• English
• Paperback 978-0-443-26484-9

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• eBook 978-0-443-26485-6

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Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development.Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning.

Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems

• 1st Edition
• January 1, 2025
• David A. Wood
• English
• Paperback 978-0-443-26510-5

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• eBook 978-0-443-26511-2

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Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores machine and deep learning models for subsurface geological prediction problems commonly encountered in applied resource evaluation and reservoir characterization tasks. The book provides insights into how the performance of ML/DL models can be optimized—and sparse datasets of input variables enhanced and/or rescaled—to improve prediction performances. A variety of topics are covered, including regression models to estimate total organic carbon from well-log data, predicting brittleness indexes in tight formation sequences, trapping mechanisms in potential sub-surface carbon storage reservoirs, and more.Each chapter includes its own introduction, summary, and nomenclature sections, along with one or more case studies focused on prediction model implementation related to its topic.

Data Analytics and Artificial Intelligence for Earth Resource Management

• 1st Edition
• November 6, 2024
• Deepak Kumar + 2 more
• English
• Paperback 978-0-443-23595-5

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• eBook 978-0-443-23596-2

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Data Analytics and Artificial Intelligence for Earth Resource Management offers a detailed look at the different ways data analytics and artificial intelligence can help organizations make better-informed decisions, improve operations, and minimize the negative impacts of resource extraction on the environment. The book explains several different ways data analytics and artificial intelligence can improve and support earth resource management. Predictive modeling can help organizations understand the impacts of different management decisions on earth resources, such as water availability, land use, and biodiversity.Resource monitoring tracks the state of earth resources in real-time, identifying issues and opportunities for improvement. Providing managers with real-time data and analytics allows them to make more informed choices. Optimizing resource management decisions help to identify the most efficient and effective ways to allocate resources. Predictive maintenance allows organizations to anticipate when equipment might fail and take action to prevent it, reducing downtime and maintenance costs. Remote sensing with image processing and analysis can be used to extract information from satellite images and other remote sensing data, providing valuable information on land use, water resources, and other earth resources.

Intelligence Systems for Earth, Environmental and Planetary Sciences

• 1st Edition
• July 30, 2024
• Hossein Bonakdari + 1 more
• English
• Paperback 978-0-443-13293-3

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• eBook 978-0-443-13292-6

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Intelligence Systems for Earth, Environmental and Planetary Sciences: Methods, Models and Applications provides cutting-edge theory and applications of modern-day artificial intelligence and data science in the Earth, environment, and planetary science fields. The book is divided into three sections: (i) Methods, covering the fundamentals of intelligence systems, along with an introduction to the preparation of datasets; (ii) Models, detailing model development, data assimilation, and techniques in each field; and (iii) Applications, presenting case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives.Intelligence Systems for Earth, Environmental and Planetary Sciences will be of interest to students, academics, and postgraduate professionals in the field of applied sciences, Earth, environmental, and planetary sciences and would also serve as an excellent companion resource to courses studying artificial intelligence applications for theoretical and practical studies in Earth, environmental, and planetary sciences.

Advances in Machine Learning and Image Analysis for GeoAI

• 1st Edition
• April 3, 2024
• Saurabh Prasad + 2 more
• English
• Paperback 978-0-443-19077-3

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• eBook 978-0-443-19078-0

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Advances in Machine Learning and Image Analysis for GeoAI presents recent advances in applications and algorithms that are at the intersection of Geospatial imaging and Artificial Intelligence (GeoAI). The book covers algorithmic advances in geospatial image analysis, sensor fusion across modalities, few-shot open-set recognition, explainable AI for Earth Observations, self-supervised learning, image superresolution, Visual Question Answering, and spectral unmixing, among other topics. This book offers a comprehensive resource for graduate students, researchers, and practitioners in the area of geospatial image analysis. It provides detailed descriptions of the latest techniques, best practices, and insights essential for implementing deep learning strategies in GeoAI research and applications.

Machine Learning in Earth, Environmental and Planetary Sciences

• 1st Edition
• June 27, 2023
• Hossein Bonakdari + 2 more
• English
• Paperback 978-0-443-15284-9

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• eBook 978-0-443-15285-6

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Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation. This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results.

Geoinformatics for Geosciences

• 1st Edition
• May 26, 2023
• Nikolaos Stathopoulos + 2 more
• English
• Paperback 978-0-323-98983-1

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• eBook 978-0-323-95782-3

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Geoinformatics for Geosciences: Advanced Geospatial Analysis using RS, GIS and Soft Computing is a comprehensive guide to the methodologies and techniques that can be used in Earth observation data assessments, geospatial analysis, and soft computing in the geosciences. The book covers a variety of spatiotemporal problems and topics in the areas of the environment, geohazards, urban analysis, health, pollution, climate change, resources and geomorphology, among others. Sections cover environmental and climate issues, analysis of geomorphological data, hazard and disaster impacts, natural and human resources, the influence of environmental conditions, geohazards, climate change, geomorphological changes, etc., and socioeconomic challenges. Detailing up-to-date techniques in geoinformatics, this book offers in-depth, up-to-date methodologies for researchers and academics to understand how contemporary data can be combined with innovative techniques and tools in order to address challenges in the geosciences.

Case Studies in Geospatial Applications to Groundwater Resources

• 1st Edition
• October 21, 2022
• Pravat Kumar Shit + 2 more
• English
• Paperback 978-0-323-99963-2

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• eBook 978-0-323-99964-9

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Case Studies in Geospatial Applications to Groundwater Resources provides thorough the most up-to-date techniques in GIS and geostatistics as they relate to groundwater, through detailed case studies that prove real-world applications of remote sensing applications to this subject. Groundwater is the primary source of fresh water in many parts of the world, while come regions are becoming overly dependent on it, consuming groundwater faster than it is naturally replenished and causing water tables to decline unremittingly. India is the largest user of groundwater in the world followed by China and the USA, with developing countries using groundwater at an unsustainable rate. Systematic planning of groundwater usage using modern techniques is essential for the proper utilization, management and modeling of this precious but shrinking natural resource. With the advent of powerful and highspeed personal computers, efficient techniques for water management have evolved, of which remote sensing, GIS (Geographic Information Systems), GPS (Global Positioning Systems) and Geostatistical techniques are of great significance. This book advances the scientific understanding, development, and application of geospatial technologies related to water resource management. Case Studies in Geospatial Applications to Groundwater Resources is a valuable reference for researchers and postgraduate students in Earth and Environmental Sciences, especially GIS, agriculture, hydrology, natural resources, and soil science, who need to be able to apply the latest technologies in groundwater research in a practical manner.