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This book focuses on the use of open source software for geospatial analysis. It demonstrates the effectiveness of the command line interface for handling both vector, raster and 3D geospatial data. Appropriate open-source tools for data processing are clearly explained and discusses how they can be used to solve everyday tasks. A series of fully worked case studies are presented including vector spatial analysis, remote sensing data analysis, landcover classification and LiDAR processing. A hands-on introduction to the application programming interface (API) of GDAL/OGR in Python/C++ is provided for readers who want to extend existing tools and/or develop their own software.
This book contains papers presented at the first Open Source Geospatial Research Symposium held in Nantes City, France, 8-10 July, 2009. It brings together insights and ideas in the fields of Geospatial Information and Geoinformatics. It demonstrates the scientific community dynamism related to open source and free software as well as in defining new concepts, standards or tools.
Since the first edition of Open Source GIS: A GRASS GIS Approach was published in 2002, GRASS has undergone major improvements. This second edition includes numerous updates related to the new development; its text is based on the GRASS 5.3 version from December 2003. Besides changes related to GRASS 5.3 enhancements, the introductory chapters have been re-organized, providing more extensive information on import of external data. Most of the improvements in technical accuracy and clarity were based on valuable feedback from readers. Open Source GIS: A GRASS GIS Approach, Second Edition, provides updated information about the use of GRASS, including geospatial modeling with raster, vector, and site data, image processing, visualization, and coupling with other open source tools for geostatistical analysis and web applications. A brief introduction to programming within GRASS encourages new development. The sample data set used throughout the book has been updated and is available on the GRASS web site. This book also includes links to sites where the GRASS software and on-line reference manuals can be downloaded and additional applications can be viewed.
Desktop Geographic Information System (GIS) software gives you the ability to make maps and analyze geographic information. This book provides a foundational level of knowledge for understanding GIS and the open source desktop mapping applications that are available for use, for free, today. Learn about vector and raster data, how to convert data, interacting with spatial databases, creating new map data, geoprocessing, scripting, and more. Special sections include focused learning on the Quantum GIS and GRASS GIS software platforms but other packages are also introduced. The Geospatial Desktop is written by the creator of Quantum GIS, so you can rest assured that you will be led by one of the most knowledgeable authors on the subject.
This book introduces the usage, functionality, and application of data in geographic information systems (GIS) for geo-spatial analysis. It offers knowledge on GIS tools and techniques and explains how they can be applied in real-world project to architects and planners in the Indian and the Greater South Asian context using open-source software. The volume explains concepts on planning and architectural tasks, their data, methods and requirements followed, and includes GIS-related exercises on the same tasks. It takes the reader through the concepts of geo-spatial analysis and its referencing system while quoting examples from India. Further, the content of the book will help the planners involved in preparing GIS-based master planning for cities under the Atal Mission for Rejuvenation and Urban Transformation (AMRUT) scheme (see Glossary for details). A practical guidebook providing a step-by-step guide to learn open source GIS, this book will be useful for students, scholars and professionals from the field of architecture and planning, geography and other spatial sciences, instructors of GIS courses on planning and architecture, urban and regional planners, transport planners, urban design, landscape architects, environmental planners, departments of town and country planning, and development authorities. It will also be useful for anyone interested in the geospatial analysis.
"Desktop GIS" explores the world of Open Source GIS software and provides a guide to navigate the many options available. Strategies for choosing a platform, selecting the right tools, integration, managing change, and getting support are presented.
Tool Up! Become a data management superstar with tools from the Geospatial Data Abstraction Library (GDAL). This book is a reference guide for quickly finding the right syntax and example usage of all GDAL/OGR commands. Used behind most of the open source geospatial applications, as well as leading proprietary GIS applications, GDAL is the preeminent spatial data access library. GDAL comes with several powerful command line utilities including tools for examining, converting, transforming, building and analyzing raster and vector data. Included within is substantial new content, supplementing the GDAL documentation which makes up the rest of the book. Look up a workflow concept like "Translate Vectors" and quickly find examples designed to get you going right away. Digital versions of the book are fully linked with bookmarks between topics and command names, making it easier than ever to follow from an example to more detailed documentation. Tooling up your skills with this book will allow you to confidently tackle future raster and vector data management challenges!
'Web Mapping Illustrated' shows readers how to create maps, even interactive maps, with free tools, including MapServer, OpenEV, GDAL/OGR, and PostGIS. It also explains how to find, collect, understand, use, and share mapping data
Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/.
In today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.