Open Source Geospatial Tools [electronic resource] : Applications in Earth Observation / by Daniel McInerney, Pieter Kempeneers.

By:

McInerney, Daniel [author.]

Contributor(s):

Series: Earth Systems Data and ModelsPublisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: XXVII, 358 p. 96 illus., 50 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319018249

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

550 23

LOC classification:

GB3-5030

Online resources:

Click here to access online

Contents:

Introduction -- Vector data processing -- Raster data explained -- Introduction to GDAL utilities -- Manipulating raster data -- Indexed color images -- Image overviews, tiling and pyramids -- Image (re-)projections and merging -- Raster meets vector data -- Raster meets point data -- Virtual rasters and raster calculations -- Pktools -- Orfeo Toolbox -- Write your own geospatial utilities -- 3D point cloud data processing -- Case study on Vector Spatial analysis -- Multispectral land cover classification -- Case study on point data -- Conclusions and future outlook.

In: Springer eBooksSummary: 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.

Item type:

eBooks

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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.