This volume fills a research gap between the rapid development of High Performance Computing (HPC) approaches and their geospatial applications. With a focus on geospatial applications, the book discusses in detail how researchers apply HPC to tackle their geospatial problems. Based on this focus, the book identifies the opportunities and challenges revolving around geospatial applications of HPC. Readers are introduced to the fundamentals of HPC, and will learn how HPC methods are applied in various specific areas of geospatial study. The book begins by discussing theoretical aspects and methodological uses of HPC within a geospatial context, including parallel algorithms, geospatial data handling, spatial analysis and modeling, and cartography and geovisualization. Then, specific domain applications of HPC are addressed in the contexts of earth science, land use and land cover change, urban studies, transportation studies, and social science. The book will be of interest to scientists and engineers who are interested in applying cutting-edge HPC technologies in their respective fields, as well as students and faculty engaged in geography, environmental science, social science, and computer science.

This book elucidates how cyberGIS (that is, new-generation geographic information science and systems (GIS) based on advanced computing and cyberinfrastructure) transforms computation- and data-intensive geospatial discovery and innovation. It comprehensively addresses opportunities and challenges, roadmaps for research and development, and major progress, trends, and impacts of cyberGIS in the era of big data. The book serves as an authoritative source of information to fill the void of introducing this exciting and growing field. By providing a set of representative applications and science drivers of cyberGIS, this book demonstrates how cyberGIS has been advanced to enable cutting-edge scientific research and innovative geospatial application development. Such cyberGIS advances are contextualized as diverse but interrelated science and technology frontiers. The book also emphasizes several important social dimensions of cyberGIS such as for empowering deliberative civic engagement and enabling collaborative problem solving through structured participation. In sum, this book will be a great resource to students, academics, and geospatial professionals for leaning cutting-edge cyberGIS, geospatial data science, high-performance computing, and related applications and sciences.

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.

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.

Many regions around the world suffer from a lack of authoritatively-collected data on factors critical to understanding human well-being. This challenges our ability to understand the progress society is making towards reducing poverty, improving lifespans, or otherwise improving livelihoods. A growing body of research is exploring how deep learning algorithms can be used to produce novel estimates of sparse development data, and how access to such data can impact development efforts. This dissertation contributes to this literature in three parts. First, using Landsat 8 satellite imagery and data from the Armed Conflict Location & Event Data Project, convolutional neural networks are trained to predict locations where conflict is likely to result in fatalities for one year. Second, building on the findings in chapter 1, this dissertation explores the potential to extend predictions to a time series using both yearly and six month intervals. Finally, chapter 3 introduces GeoQuery, a dynamic web application which utilizes a High Performance Computing cluster and novel parallel geospatial data processing methods to overcome challenges associated with integrating, and distributing geospatial data within research communities.

Solutions for Time-Critical Remote Sensing Applications The recent use of latest-generation sensors in airborne and satellite platforms is producing a nearly continual stream of high-dimensional data, which, in turn, is creating new processing challenges. To address the computational requirements of time-critical applications, researchers

The book deals with the most recent technology of distributed computing.As Internet continues to grow and provide practical connectivity between users of computers it has become possible to consider use of computing resources which are far apart and connected by Wide Area Networks.Instead of using only local computing power it has become practical to access computing resources widely distributed. In some cases between different countries in other cases between different continents.This idea of using computer power is similar to the well known electric power utility technology. Hence the name of this distributed computing technology is the Grid Computing.Initially grid computing was used by technologically advanced scientific users.They used grid computing to experiment with large scale problems which required high performance computing facilities and collaborative work.In the next stage of development the grid computing technology has become effective and economically attractive for large and medium size commercial companies.It is expected that eventually the grid computing style of providing computing power will become universal reaching every user in industry and business. * Written by academic and industrial experts who have developed or used grid computing* Many proposed solutions have been tested in real life applications* Covers most essential and technically relevant issues in grid computing