An edited book entitled “High Performance Computing for Geospatial Applications” has been approved by Springer. Editors are Drs. Wenwu Tang (University of North Carolina at Charlotte) and Shaowen Wang (University of Illinois at Urbana-Champaign).
Here is the background information of the book. High Performance Computing (HPC) allows for the use of advanced computer systems to accelerate and enable intensive computation of scientific or engineering problems, in general, and geospatial problems in particular. As the rapid development of multi- and many-core computing architecture, HPC resources (broadly including computing clusters, computing grids, and cloud computing) and technologies are increasingly available. The utility of HPC in empowering and enhancing domain-specific problem-solving has been extensively recognized. Over the past decade, HPC has been increasingly introduced into geospatial applications to resolve computational challenges.
The objective of this edited book is to fill the gap between the rapid development of HPC approaches and their geospatial applications that are often lagged behind. This book will examine the history and status quo of HPC, and their utility in the resolution of complex geospatial problems. With a focus on geospatial applications, this book will discuss in detail how researchers apply HPC to tackle their geospatial problems. Based on this focus, the book will identify the opportunities and challenges revolving around geospatial applications of HPC.
This book will cover five parts.
Part I: Fundamental of HPC and related algorithms for geospatial applications.
Part II: Geospatial data handling and/or remote sensing driven by HPC.
Part III: Spatial analysis and modeling (e.g., statistics, optimization, and simulation), and cartography and geovisualization within the context of HPC.
Part IV: Domain applications of HPC, including (but not limited to): ecology, land use and land cover change, urban studies, spatial epidemiology, earth science, environmental engineering, transportation studies, and social science.
Part V: Vision and future of HPC for geospatial applications.