The scope of image processing and recognition has broadened due to the gap in scientific visualization. Thus, new imaging techniques have developed, and it is imperative to study this progression for optimal utilization. Big Data Analytics for Satellite Image Processing and Remote Sensing is a critical scholarly resource that examines the challenges and difficulties of implementing big data in image processing for remote sensing and related areas. Featuring coverage on a broad range of topics, such as distributed computing, parallel processing, and spatial data, this book is geared towards scientists, professionals, researchers, and academicians seeking current research on the use of big data analytics in satellite image processing and remote sensing.

Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics.

This book thoroughly covers the remote sensing visualization and analysis techniques based on computational imaging and vision in Earth science. Remote sensing is considered a significant information source for monitoring and mapping natural and man-made land through the development of sensor resolutions that committed different Earth observation platforms. The book includes related topics for the different systems, models, and approaches used in the visualization of remote sensing images. It offers flexible and sophisticated solutions for removing uncertainty from the satellite data. It introduces real time big data analytics to derive intelligence systems in enterprise earth science applications. Furthermore, the book integrates statistical concepts with computer-based geographic information systems (GIS). It focuses on image processing techniques for observing data together with uncertainty information raised by spectral, spatial, and positional accuracy of GPS data. The book addresses several advanced improvement models to guide the engineers in developing different remote sensing visualization and analysis schemes. Highlights on the advanced improvement models of the supervised/unsupervised classification algorithms, support vector machines, artificial neural networks, fuzzy logic, decision-making algorithms, and Time Series Model and Forecasting are addressed. This book guides engineers, designers, and researchers to exploit the intrinsic design remote sensing systems. The book gathers remarkable material from an international experts' panel to guide the readers during the development of earth big data analytics and their challenges.

Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians.

Big Data Analytics in Earth, Atmospheric and Ocean Sciences SPECIAL PUBLICATIONS SERIES Big Data Analytics in Earth, Atmospheric, and Ocean Sciences An ever-increasing volume of Earth data is being gathered. These data are “big” not only in size but also in their complexity, different formats, and varied scientific disciplines. As such, big data are disrupting traditional research. New methods and platforms, such as the cloud, are tackling these new challenges. Big Earth Data Analytics explores new tools for the analysis and display of the rapidly increasing volume of data about the Earth. Volume highlights include: An introduction to the breadth of big earth data analytics Architectures developed to support big earth data analytics Different analysis and statistical methods for big earth data Current applications of analytics to Earth science data Challenges to fully implementing big data analytics The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

This book studies the evolution of sustainable green smart cities and demonstrates solutions for green environmental issues using modern industrial IoT solutions. It is a ready reference with guidelines and a conceptual framework for context-aware product development and research in the IoT paradigm and Big Data Analytics for a Green Environment. It brings together the most recent advances in IoT and Big Data in Green Environments, emerging aspects of the IoT and Big Data for Green Cities, explores key technologies, and develops new applications in this research field. Key Features: • Discusses the framework for development and research in the IoT Paradigm and Big Data Analytics. • Highlights threats to the IoT architecture and Big Data Analytics for a Green Environment. • Present the I-IoT architecture, I-IoT applications, and their characteristics for a Green Environment. • Provides a systematic overview of the state-of-the-art research efforts. • Introduces necessary components and knowledge to become a vital part of the IoT revolution for a Green Environment. This book is for professionals and researchers interested in the emerging technology of sustainable development, green cities, and Green Environment.

Planetary health involves complex spatial–temporal interactions among agents, hosts, and earth environment. Due to rapid technical development of geomatics, including geographic information systems (GIS) and remote sensing (RS) in the era of big data analytics, therefore, earth data analytics has become one of the important approaches for monitoring earth surface process and measuring of the effects of environment changes on all humans and other living organisms on earth. Various methods in earth data analytics, including spatial–temporal statistics, spatial evolutionary algorithms, remote sensing image analysis, wireless geo-sensors, and location-based analytics, are an emerging discipline in understanding complex interactions in planetary health. This edited book provides a broad focus on methodological theories of earth data analytics and their applications to measuring the process of planetary health, with the goal to build scientific understanding on how geospatial analytics can provide valuable insights in measuring environmental risks in Southeast Asian regions. It is collection of selected papers covering both theoretical and empirical studies focusing on topics relevant to spatial perspectives on planetary health and environmental exposure studies. The book is written for senior undergraduates, graduate students, lecturers, and researchers in applications of geospatial technologies for public health and environmental studies.

This monograph provides comprehensive coverage of the collection, management, and use of big data obtained from remote sensing. The book begins with an introduction to the basics of big data and remote sensing, laying the groundwork for the more specialized information to follow. The volume then goes on to address a wide variety of topics related to the use and management of remote sensing big data, including hot topics such as analysis through machine learning, cyberinfrastructure, and modeling. Examples on how to use the results of big data analysis of remotely sensed data for concrete decision-making are offered as well. The closing chapters discuss geospatial big data initiatives throughout the world and future challenges and opportunities for remote sensing big data applications. The audience for this book includes researchers at the intersection of geoscience and data science, senior undergraduate and graduate students, and anyone else interested in how large datasets obtained through remote sensing can be best utilized. The book presents a culmination of 30 years of research from renowned spatial scientists Drs. Liping Di and Eugene Yu.

Geospatial data acquisition and analysis techniques have experienced tremendous growth in the last few years, providing an opportunity to solve previously unsolved environmental- and natural resource-related problems. However, a variety of challenges are encountered in processing the highly voluminous geospatial data in a scalable and efficient manner. Technological advancements in high-performance computing, computer vision, and big data analytics are enabling the processing of big geospatial data in an efficient and timely manner. Many geospatial communities have already adopted these techniques in multidisciplinary geospatial applications around the world. This book is a single source that offers a comprehensive overview of the state of the art and future developments in this domain. FEATURES Demonstrates the recent advances in geospatial analytics tools, technologies, and algorithms Provides insight and direction to the geospatial community regarding the future trends in scalable and intelligent geospatial analytics Exhibits recent geospatial applications and demonstrates innovative ways to use big geospatial data to address various domain-specific, real-world problems Recognizes the analytical and computational challenges posed and opportunities provided by the increased volume, velocity, and veracity of geospatial data This book is beneficial to graduate and postgraduate students, academicians, research scholars, working professionals, industry experts, and government research agencies working in the geospatial domain, where GIS and remote sensing are used for a variety of purposes. Readers will gain insights into the emerging trends on scalable geospatial data analytics.

Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors’ perspective. Key Features of Volume I: Provides the fundamentals of hyperspectral remote sensing used in agricultural crops and vegetation studies. Discusses the latest advances in hyperspectral remote sensing of ecosystems and croplands. Develops online hyperspectral libraries, proximal sensing and phenotyping for understanding, modeling, mapping, and monitoring crop and vegetation traits. Implements reflectance spectroscopy of soils and vegetation. Enumerates hyperspectral data mining and data processing methods, approaches, and machine learning algorithms. Explores methods and approaches for data mining and overcoming data redundancy; Highlights the advanced methods for hyperspectral data processing steps by developing or implementing appropriate algorithms and coding the same for processing on a cloud computing platform like the Google Earth Engine. Integrates hyperspectral with other data, such as the LiDAR data, in the study of vegetation. Includes best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, crop productivity and water productivity mapping, and modeling.