# open-geo-tutorial

**Repository Path**: zfsrt/open-geo-tutorial

## Basic Information

- **Project Name**: open-geo-tutorial
- **Description**: Tutorial of basic remote sensing and GIS methodologies using open source software (GDAL in Python or R)
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 1
- **Created**: 2019-12-08
- **Last Updated**: 2024-11-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Open Source Geoprocessing Tutorial
==================================

<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">
<img
    alt="Creative Commons License"
    style="border-width:0"
    src="https://i.creativecommons.org/l/by-sa/4.0/80x15.png" />
</a><br />
<span xmlns:dct="http://purl.org/dc/terms/" property="dct:title"></a>

# UPDATE
Check out an updated and extended version of this tutorial written by @patrickcgray. It's amazing and uses more "Pythonic" `rasterio`/`fiona`/`shapely` libraries instead of `GDAL`/`OGR`/`OSR` directly. It's amazing!

[patrickcgray/open-geo-tutorial](https://github.com/patrickcgray/open-geo-tutorial)

# Introduction
Tutorial of basic remote sensing and GIS methodologies using open source
software (GDAL in Python). Tutorial covers workflow to perform image
classification using machine learning classifiers:

0. Introduction
   [[Python](http://ceholden.github.io/open-geo-tutorial/python/chapter_0_introduction.html)]
[[R](http://ceholden.github.io/open-geo-tutorial/R/chapter_0_introduction.html)]
1. The GDAL datatypes and objects
   [[Python](http://ceholden.github.io/open-geo-tutorial/python/chapter_1_GDALDataset.html)]
[[R](http://ceholden.github.io/open-geo-tutorial/R/chapter_1_GDAL.html)]
2. Your first vegetation index
   [[Python](http://ceholden.github.io/open-geo-tutorial/python/chapter_2_indices.html)]
[[R](http://ceholden.github.io/open-geo-tutorial/R/chapter_2_indices.html)]
3. Visualizing data
   [[Python](http://ceholden.github.io/open-geo-tutorial/python/chapter_3_visualization.html)]
[[R](http://ceholden.github.io/open-geo-tutorial/R/chapter_3_visualization.html)]
4. Vector data - the OGR library
   [[Python](http://ceholden.github.io/open-geo-tutorial/python/chapter_4_vector.html)]
[[R](http://ceholden.github.io/open-geo-tutorial/R/chapter_4_vector.html)]
5. Land cover classification
   [[Python](http://ceholden.github.io/open-geo-tutorial/python/chapter_5_classification.html)]
[[R](http://ceholden.github.io/open-geo-tutorial/R/chapter_5_classification.html)]


# Download

Materials and data for these lessons are included inside this repository under
the `example` folder. I would recommend downloading all of the lesson material
at once, instead of downloading individual files.

Two ways to download the entire repository include:

* Use `git` to `clone` the repository (recommended)
    * [Instructions](https://help.github.com/articles/cloning-a-repository/)
* Downloading and extracting a ZIP file of the 'master' branch
    * [Download link](https://github.com/ceholden/open-geo-tutorial/archive/master.zip)

A caution:

> Be careful trying to "right-click" and save files from repositories on
> Github, because this will save a HTML file linking to the file instead of
> the file itself. This has been a common source of confusion among people,
> especially if they are new to Github. It is possible to download individual
> files from a Github repository website, and can be done by clicking on a
> file listed on the repository site, and then clicking the "Download" button
> at the top right of the next web page (also next to the "History" button).


# R Installation

The following R libraries will be needed for this tutorial:

- `raster`
- `rgdal`
- `sp`
- `randomForest`

Install them from within R as follows:

``` r install.packages(c('raster', 'rgdal', 'sp', 'randomForest')) ```

# Python Installation

Thanks to the lovely people at [binder](http://mybinder.org/), you can try
running the lessons on their servers via magic:

[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/ceholden/open-geo-tutorial)

To run the Jupyter Notebooks (formerly known as IPython Notebooks) and follow
the tutorial locally, you will need to install Python and the libraries used in
the tutorials. This installation can be accomplished in many ways, but I will
list the two most common approaches:

### conda

I recommend using the [Anaconda](http://conda.pydata.org/docs/) Python
distribution to make installation of the tutorial dependencies less complicated.
After [installing Anaconda or "miniconda" by following their
instructions](http://conda.pydata.org/docs/install/quick.html), you can install
the dependencies as follows:

``` bash
conda env create -f environment.yml
source activate open-geo-tutorial
```

### pip

Assuming you already have Python installed, you could use the the Python package
manager, [pip](https://en.wikipedia.org/wiki/Pip_(package_manager)), to install
the dependencies.

Following "pip" convention, you can find all package requirements in the
`requirements.txt` file. I would also recommend installing these packages into a
virtual environment to avoid conflicts with existing versions of the required
Python packages. To isolate these dependencies from the rest of your system, use
[virtualenv](https://virtualenv.pypa.io/en/latest/installation.html):

``` bash
# Create virtual environment to isolate dependencies
virtualenv venv
# Activate virtual environment
source venv/bin/activate
# Install dependencies pip
install -r requirements.txt
```

You will need to have GDAL installed for Python to build the drivers against.
You may have the Python bindings already built as part of GDAL's installation
process.