gdal_hillshade_tutorial - Tutorial for rendering hillshades with GDAL

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Participants will learn how to work with Digital Elevation Model data and use GDAL to generate a Shaded Relief / Hillshade for the Kings Canyon National Park area, in the southern Sierra Nevada mountain range, California. The commands in this tutorial are meant to be run in the Bash shell on Mac OS X or a Linux OS but these processes can also be accomplished using QGIS. Ths tutorial assumes you have GDAL installed and that it is accessible from a Command Line Interface such as the Terminal App. Some familiarity with the Unix CLI is beneficial but not required.



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node-gdal - Node.js bindings for GDAL (Geospatial Data Abstraction Library)

  •    C++

Read and write raster and vector geospatial datasets straight from Node.js with this native GDAL binding. GDAL 2.0.1 (GEOS 3.4.2, Proj.4 4.8.0) comes bundled, so node-gdal will work straight out of the box. To get started, browse the API Documentation or examples. This binding is a collaboration between Natural Atlas and Mapbox. Its contributors are Brandon Reavis, Brian Reavis, Dane Springmeyer, Zac McCormick, and others.



GDAL SSIS is a collection of geospatial components for SQL Server Integration Services (SSIS) that leverages GDAL to support a large number of GIS data formats.

gdal - GDAL is an open source X/MIT licensed translator library for raster and vector geospatial data formats

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GDAL is an open source X/MIT licensed translator library for raster and vector geospatial data formats.

PDAL - PDAL is Point Data Abstraction Library. GDAL for point cloud data.

  •    C++

PDAL is Point Data Abstraction Library. GDAL for point cloud data.

ALK-EDBS to WKT-Database Konverter

  •    VB

Konverting the GIS-Format 'EDBS' to Spatial-Database 'PostGIS' using the 'Well Known Text'-Interface. EDBS is a German Fileformat from ALK (Automatisierte Liegenschaftskarte). So this GIS can be used for UNM-Mapserver and otehr OGC-conform GIS-Programms. Update: After ALK in Germany ist replaced by ALKIS, edbs2wkt will not be further developed. Use (gdal/ogr) for ALKIS-Interface-Format quot;NASquot;.

earthenterprise - Google Earth Enterprise - Open Source

  •    Javascript

Earth Enterprise is the open source release of Google Earth Enterprise, a geospatial application which provides the ability to build and host custom 3D globes and 2D maps. Earth Enterprise does not provide a private version of Google imagery that's currently available in Google Maps or Earth.Refer to the wiki for instructions on building from source on one of these platforms.

rasterio - Rasterio reads and writes geospatial raster datasets

  •    Python

Rasterio reads and writes geospatial raster data.Geographic information systems use GeoTIFF and other formats to organize and store gridded, or raster, datasets. Rasterio reads and writes these formats and provides a Python API based on N-D arrays.


  •    C

GtkGis is a Gtk+ widget to embed basic GIS functionalities inside applications. It requires GooCanvas, libxml and Gdal.


  •    Python

Geo-Spatial Data Viewer (GSDView) is a lightweight viewer for geo-spatial data and products. It is written in python and Qt4 and uses the GDAL library. GSDView is modular and has a simple plug-in architecture.

Creating-maps-in-R - Introductory tutorial on graphical display of geographical information in R.

  •    TeX

This tutorial is an introduction to visualising and analysing spatial data in R based on the sp class system. For a guide to the more recent sf package check out Chapter 2 of the in-development book Geocomputation with R, the source code of which can be found at Although sf supersedes sp in many ways, there is still merit in learning the content in this tutorial, which teaches principles that will be useful regardless of software. Specifically this tutorial focusses on map-making with R's 'base' graphics and various dedicated map-making packages for R including tmap and leaflet. It aims to teach the basics of using R as a fast, user-friendly and extremely powerful command-line Geographic Information System (GIS).

Gdal to Tiles C#


Make tiles images for use Google Earth. Use KML superoverlay methods.


  •    C++

Meteosat OpenMTP/HRI/HRIT C++ access libraries. This libraries allows programs to read geostationary EUMETSAT native formats. It also contains GDAL drivers and conversion programs.

GDAL Package for R

  •    C

WARNING: rgdal is only available for download from CRAN - this repository is only used for development and browsing of source code. NEVER use the very out of date file bundles for download from this site!!! See for link.


  •    Java

iGOR: interactive graphical ogr reprojector. iGOR is a windows graphical user interface for the GDAL ogr2ogr {} developed in Java.

csvkit - A suite of utilities for converting to and working with CSV, the king of tabular file formats

  •    Python

csvkit is a suite of command-line tools for converting to and working with CSV, the king of tabular file formats. It is inspired by pdftk, GDAL and the original csvcut tool by Joe Germuska and Aaron Bycoffe.

useR-machine-learning-tutorial - useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016

  •    Jupyter

Instructions for how to install the necessary software for this tutorial is available here. Data for the tutorial can be downloaded by running ./data/ (requires wget). Certain algorithms don't scale well when there are millions of features. For example, decision trees require computing some sort of metric (to determine the splits) on all the feature values (or some fraction of the values as in Random Forest and Stochastic GBM). Therefore, computation time is linear in the number of features. Other algorithms, such as GLM, scale much better to high-dimensional (n << p) and wide data with appropriate regularization (e.g. Lasso, Elastic Net, Ridge).

statistical-analysis-python-tutorial - Statistical Data Analysis in Python

  •    HTML

Chris Fonnesbeck is an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He originally hails from Vancouver, BC and received his Ph.D. from the University of Georgia. This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to bootstrapping methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.

winerama-recommender-tutorial - A wine recommender system tutorial using Python technologies such as Django, Pandas, or Scikit-learn, and others such as Bootstrap

  •    Python

This repository contains the code for a wine reviews and recommendations web application, in different stages as git tags. The idea is that you can follow the tutorials through the tags listed below, and learn the different concepts explained in them. The tutorials include instructions on how to deploy the web using a Koding account. However, Koding recently moved from solo to team accounts and the link provided to my Koding account deployment of the tutorial result is not working anymore. The tutorial can still be followed with no problem at all. The following tutorials will guide you through each of the previous Git tags while learning different concepts of data product development with Python.

Tutorial: ADO.NET Data Services (with Source Code)


Tutorial: ADO.NET Data Services (with Source Code) Find more explications and a more detailed step-by-step guide on my Blog:

pycon-2016-tutorial - Machine Learning with Text in scikit-learn

  •    Jupyter

Presented by Kevin Markham at PyCon on May 28, 2016. Watch the complete tutorial video on YouTube. Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn.

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