Merge branch 'johanvdw:osgeolive'
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"source": [
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"<b><h1 style=\"margin: 0.0px 0.0px 0.0px 0.0px; line-height: 15.4px; font: 22.0px 'Lucida Sans'; color: #004d87; -webkit-text-stroke: #004d87; background-color: #ffffff\"><span class=\"s1\">GDAL/OGR Quickstart</span></h1></b>\n",
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"\n",
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"The first Notebook is dedicated to the use of the Geospatial Data Abstraction Library (**GDAL**) from the bash command line. GDAL is a powerful translator library for raster and vector geospatial data formats it presents a single raster abstract data model and vector abstract data model to the calling application for all supported formats.\n",
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"The first Notebook is dedicated to the use of the Geospatial Data Abstraction Library (**GDAL**) from the bash command line. GDAL is a powerful translator library for raster and vector geospatial data formats. It presents a single raster abstract data model and vector abstract data model for all supported formats.\n",
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"\n",
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"This Notebook is derived from the original [GDAL-OGR quickstart](http://localhost/osgeolive/en/quickstart/gdal_quickstart.html) adapted to run interactively in an IPython notebook and is composed by two main parts **GDAL** (to handle raster data) and **OGR** (to work with vector data)\n",
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"\n",
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"You will find the demo data at ```/usr/local/share/data```. We want to have a look at the [naturalearth dataset](http://localhost/osgeolive/en/overview/naturalearth_overview.html) data in this quickstart. We want to work with a copy of the data. So the first step is to copy the data to your home directory."
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* **Import IPython utility to display images**"
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]
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"OUTPUTDIR='/home/user/jupyter/notebooks/GSOC/OUTPUT/'"
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"OUTPUTDIR='/home/user/jupyter/notebooks/GSoC-2015/OUTPUT/'"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook is heavly based on the [Python GDAL/OGR Cookbook](https://pcjericks.github.io/py-gdalogr-cookbook/)"
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"This notebook is heavily based on the [Python GDAL/OGR Cookbook](https://pcjericks.github.io/py-gdalogr-cookbook/)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this first example we'll learn how to generate a proper OGR geometry of type \"Geometry Collection\" which allow us to store multiple types of geometry (point, line, polygon) in a single vector file (note: this is a great advantage compared with the \"standard but obsolete\" ESRI Shape File data format, which is limited to only one type of geometry). "
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"In this first example we'll learn how to generate a proper OGR geometry of type \"Geometry Collection\" which allows us to store multiple types of geometry (point, line, polygon) in a single vector file (note: this is a great advantage compared with the \"standard but obsolete\" ESRI Shape File data format, which is limited to only one type of geometry). "
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"And finally export the data as GeoJson string"
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"And finally export the data as GeoJSON string and paste it online for easy visualization on github:"
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]
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{
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"* [GDAL](http://localhost/osgeolive/en/overview/gdal_overview.html) (Geospatial Data Abstraction Library) \n",
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"* [OSSIM](http://localhost/osgeolive/en/overview/ossim_overview.html) (Open Source Software Image Mapping). \n",
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"\n",
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"Both libraries are the state of the art in software for reading and traslating geospatial data between several formats, tools for reprojection, mosaic and subset geospatial dataset.\n",
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"Both libraries are the state of the art in software for reading and translating geospatial data between several formats, and the contain tools to reproject, mosaic and subset geospatial dataset.\n",
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"\n",
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"Those two libraries have a lot in commons but differ for some particular features. While OSSIM is more oriented to photogrammetry with his hi-performance hi-precision raster processing engine, offering parallel processing support (trough MPI), an unique orthorectification syestem which enable the processing of satellite-sensor specific data products; GDAL present a complete set of software utility to work with both raster and vector including support for sql-like query processing, a very wide range of driver to access both raster and vector for all the most common data format (OSSIM can be extended to have access to GDAL trough a plugin system) and a complete set of API accessible trough sripting languages such Python.\n",
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"The two libraries have a lot in common but differ in some particular features. **GDAL** presents a complete set of software utilities to work with both raster and vector data. It includes support for sql-like query processing, a very wide range of driver to access both raster and vector for all the most common data formats and a complete set of API accessible trough scripting languages such Python. **OSSIM** is more oriented to photogrammetry with its hi-performance hi-precision raster processing engine. It offers parallel processing support, an unique orthorectification system which enables the processing of satellite-sensor specific data products. \n",
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"\n",
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"In the notebooks below we'll explore the tools offered by the *command line utilities* for both software plus the Python scripting capabilities pf GDAL.\n",
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"\n",
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"outputs": [],
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"source": [
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"DATADIR='/home/user/data/landsat/'\n",
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"OUTPUTDIR='/home/user/jupyter/notebooks/GSOC/OUTPUT/'"
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"OUTPUTDIR='/home/user/jupyter/notebooks/GSoC-2015/OUTPUT/'"
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]
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},
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"source": [
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"# Introduction\n",
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"\n",
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"This GSoC 2015 Idea will focus on the development of a set of digital notebooks to explore several open source solution for geospatial data analysis, with the aim of bridging together the several software libraries already installed on the live to perform complex *geo-data-science workflows*. \n",
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"This set of digital notebooks was created to explore several open source solutions for geospatial data analysis, with the aim of bridging together the several software libraries already installed on OSGeo live to perform complex *geo-data-science workflows*. It was originally started as a Google Summer of Code (GSoC) project in 2015.\n",
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"\n",
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"The notebooks are developed in the [Jupyter notebook server](https://jupyter.org/) environments which is heavily based on the [IPython](http://ipython.org/) project, are written in different languages (bash, python, R) and are organized in a series of \"topic-oriented\" geospatial notebooks.\n",
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"The notebooks are developed in the [Jupyter notebook server](https://jupyter.org/) environment, which is heavily based on the [IPython](http://ipython.org/) project. They are written in different languages (bash, Python, R) and are organized in a series of \"topic-oriented\" geospatial notebooks.\n",
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"\n",
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"For a complete description of each projects installed on the OSGeo-Live refer to the [OSGeo-Live documentation](http://localhost/osgeolive/en/index.html). This work will focus on the usage of several scientific python libraries like [numpy](http://www.numpy.org/), [scipy](http://www.scipy.org/), [pandas](http://pandas.pydata.org/), [matplotlib](http://matplotlib.org/) on GFOSS (Geographic Free and Open Source Software) projects like [GRASS](https://grass.osgeo.org/), [GDAL](http://www.gdal.org/), [OSSIM](https://trac.osgeo.org/ossim/), [mapserver](http://mapserver.org/) and more specialized software like [R](https://www.r-project.org/) for geostatistic workflow and [postgis](http://postgis.net/) as geospatial relational database.\n",
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"For a complete description of each project installed on OSGeo-Live refer to the [OSGeo-Live documentation](http://localhost/osgeolive/en/index.html). This work will focus on different aspects:\n",
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" * The usage of several scientific Python libraries like [numpy](http://www.numpy.org/), [scipy](http://www.scipy.org/), [pandas](http://pandas.pydata.org/), [matplotlib](http://matplotlib.org/)\n",
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" * The usage of GFOSS (Geographic Free and Open Source Software) projects like [GRASS](https://grass.osgeo.org/), [GDAL](http://www.gdal.org/), [OSSIM](https://trac.osgeo.org/ossim/), [mapserver](http://mapserver.org/) \n",
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" * And finally introducing more specialized software like [R](https://www.r-project.org/) for geostatistic analyses and [postgis](http://postgis.net/) as geospatial relational database.\n",
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"\n",
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"\n",
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"The geospatial notebook here developed will make use of the sample dataset installed on the OSGeo-Live:\n",
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"The geospatial notebook here developed will make use of the sample dataset pre-installed on OSGeo-Live:\n",
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"\n",
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"* [Natural Earth](http://localhost/osgeolive/en/overview/naturalearth_overview.html)\n",
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"\n",
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"\n",
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"* [NetCDF Data Set](http://localhost/osgeolive/en/overview/netcdf_dataset_overview.html)\n",
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"\n",
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"The geospatial notebooks here are organized into several topics with the aim of exploring different geospatial libraries installed on the OSGeo-Live. The geospatial notebooks walk the user from simple usage of command line tools such GDAL, PROJ and OSSIM, to basic SQL access of database information, to more complex geoprocessing including data parsing, numerical processing, and finally, the use of a complete GIS platform.\n",
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"The geospatial notebook here are composed of 6 different sections with the aim of introducing some of the several geospatial libraries installed on OSGeo-Live. The geospatial notebooks will walk the user from simple usage of command line tools such GDAL, PROJ and OSSIM to basic SQL query on how to access database information to more complex geoprocessing including raw data parsing, numerical processing and the use of complete GIS platfom like GRASS GIS.\n",
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"\n",
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"\n",
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"# Topic Layout\n",
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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}
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