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GRASS GIS 7: your reliable geospatial number cruncher

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Titel GRASS GIS 7: your reliable geospatial number cruncher
Serientitel FOSS4G 2014 Portland
Autor Neteler, Markus
Lizenz CC-Namensnennung - keine kommerzielle Nutzung - keine Bearbeitung 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt in unveränderter Form zu jedem legalen und nicht-kommerziellen Zweck nutzen, vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/31688
Herausgeber FOSS4G
Open Source Geospatial Foundation (OSGeo)
Erscheinungsjahr 2014
Sprache Englisch
Produzent Foss4G
Open Source Geospatial Foundation (OSGeo)
Produktionsjahr 2014
Produktionsort Portland, Oregon, United States of America

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Fachgebiet Informatik
Abstract GRASS GIS (Geographic Resources Analysis Support System) looks back to the longest development history in the FOSS4G community. Having been available for 30 years, a lot of innovation has been put into the new GRASS GIS 7 release. After six years of development it offers a lot of new functionality, e.g. enhanced vector network analysis, voxel processing, a completely new engine for massive time series management, an animation tool for raster and vector map time series, a new graphic image classification tool, a "map swiper" for interactive maps comparison, and major improvements for massive data analysis (see also http://grass.osgeo.org/grass7/). The development was driven by the rapidly increasing demand for robust and modern free analysis tools, especially in terms of massive spatial data processing and processing on high-performance computing systems. With respect to GRASS GIS 6.4 more than 10,000 source code changes have since been made.GRASS GIS 7 provides a new powerful Python interface that allows users to easily create new applications that are powerful and efficient. The topological vector library has been improved in terms of accuracy, processing speed, and support for large files. Furthermore, projections of planets other than Earth are now supported as well. Many modules have been significantly optimized in terms of speed even by orders of magnitude. The presentation will showcase the new features along with real-world examples and the integration with QGIS, gvSIG CE, R statistics, and the ZOO WPS engine.
Schlagwörter GRASS GIS
geospatial
analysis
HPC
cluster
image processing
vector
raster
voxel
time series
visualization
Python
WPS

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