DEEP LEARNING FOR THE EARTH SCIENCESExplore this insightful treatment of deep learning in the field of earth sciences, from four leading voicesDeep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep ...

Earth observation is the field of science concerned with the problem of monitoring and modeling the processes on the Earth surface and their interaction with the atmosphere. The Earth is continuously monitored with advanced optical and radar sensors. The images are analyzed and processed to deliver useful products to individual users, agencies and public administrations. To deal with these problems, remote sensing image processing is nowadays a mature research area, and the techniques deve...

Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond.This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading i...

Urban Remote Sensing is designed for upper level undergraduates, graduates, researchers and practitioners, and has a clear focus on the development of remote sensing technology for monitoring, synthesis and modeling in the urban environment. It covers four major areas: the use of high-resolution satellite imagery or alternative sources of image date (such as high-resolution SAR and LIDAR) for urban feature extraction; the development of improved image processing algorithms and tec...

Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice.Includes coverage of:The NARMAX (nonlinear autoregressive moving aver...

An extensive update to a classic textStochastic geometry and spatial statistics play a fundamental role in many modern branches of physics, materials sciences, engineering, biology and environmental sciences. They offer successful models for the description of random two- and three-dimensional micro and macro structures and statistical methods for their analysis.The previous edition of this book has served as the key reference in its field ...

Advanced Remote Sensing is an application-based reference that provides a single source of mathematical concepts necessary for remote sensing data gathering and assimilation. It presents state-of-the-art techniques for estimating land surface variables from a variety of data types, including optical sensors such as RADAR and LIDAR. Scientists in a number of different fields including geography, geology, atmospheric science, environmental science, planetary science and ecology will have acc...

This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR.

Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 191.Rainfall: State of the Science offers the most up-to-date knowledge on the fundamental and practical aspects of rainfall. Each chapter, self-contained and written by prominent scientists in their respective fields, provides three forms of information: fundamental principles, detailed overview of current knowledge and description of existing methods, and emerging techniques a...

The availability of large data sets has allowed researchers to uncover complex properties such as large-scale fluctuations and heterogeneities in many networks, leading to the breakdown of standard theoretical frameworks and models. Until recently these systems were considered as haphazard sets of points and connections. Recent advances have generated a vigorous research effort in understanding the effect of complex connectivity patterns on dynamical phenomena. This book presents a compreh...

Provides up-to-date developments in the field of remote sensing by assessing scale issues in land surface, properties, patterns, and processesScale is a fundamental and crucial issue in remote sensing studies and image analysis. GIS and remote sensing scientists use various scaling techniques depending on the types of remotely sensed images and geospatial data used. Scaling techniques affect image analysis such as object identification and change detection....

Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 200.Trajectory-based (“Lagrangian”) atmospheric transport and dispersion modeling has gained in popularity and sophistication over the previous several decades. It is common practice now for researchers around the world to apply Lagrangian models to a wide spectrum of issues.Lagrangian Modeling of the Atmosphere is a comprehensive volume that includes sections on Lagrang...