Contains practical tips (one-liners) for everyday geospatial data analysis
Provides comprehensive tutorials and explanations on data handling for environmental applications
Also applicable for automated large data processing
Focuses exclusively on free open source geospatial software that includes: GDAL/OGR, Orfeo Toolbox, pktools and spdlib
Serves as a tutorial for novice command line users and a reference guide for experienced users
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.
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.
This book focuses on the use of open source software for geospatial analysis. It provides concise solutions for analyzing and visualizing spatial data as well as demonstrates the effectiveness of the command line interface for handling geospatial data.
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.
Inhaltsverzeichnis
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.
Klappentext
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.
Contains practical tips (one-liners) for everyday geospatial data analysis
Provides comprehensive tutorials and explanations on data handling for environmental applications
Also applicable for automated large data processing
Focuses exclusively on free open source geospatial software that includes: GDAL/OGR, Orfeo Toolbox, pktools and spdlib
Serves as a tutorial for novice command line users and a reference guide for experienced users
Includes supplementary material: sn.pub/extras
