Geospatial Open Source Python Libraries

This section lists open-source packages for geospatial file manipulation with Python. The necessary packages are already installed flusstools. In addition, the following paragraphs provide explanations of relevant and optional packages for this eBook and how those can be installed.

OSGeo and GDAL Including ogr and osr¶

The gdal library for raster data handling comes along with ogr for vector data handling, and osr for geospatial referencing. GDAL and OGR are managed and developed by the OSGeo Project, which is part of the Open Source Geospatial Foundation - the developers of QGIS.

The tutorials in this eBook build on gdal and ogr (including osr for spatial referencing), which is why it is important to get the installation of GDAL right:

• Linux users may follow the instructions for installing gdal and flusstools with pip and venv.

• Windows users preferably install gdal and flusstools in a conda environment through Anaconda.

Select your platform for more specific installation instructions:

conda install -c conda-forge gdal

geojson¶

The geojson library is the most direct option for handling GeoJSON data and is also already installed along with flusstools.

pip install geojson

conda install -c conda-forge geojson

Descartes Labs¶

Even though of proprietary origin, the descarteslabs library (developed and maintained by Descartes Labs) comes with many open-sourced functions. Moreover, Descartes Labs hosts the showcase platform GeoVisual Search with juicy illustrations of artificial intelligence (AI) applications in geoscience. Note that descarteslabs is not installed along with flusstools.

pip install descarteslabs

conda install -c conda-forge descartes

conda install shapely
pip install descarteslabs

Python Imaging Library (PIL) / pillow¶

Processing images with Python is enabled with the Python Imaging Library (PIL). PIL supports many image file formats and has efficient graphics processing capabilities. The pillow library is a user-friendly PIL fork and provides Image* modules (e.g., Image, ImageDraw, ImageMath, and many more). If flusstools is installed, no further action is required for working with the PIL/pillow-related contents of this eBook.

Note that the conda base environment includes PIL (test with import PIL), which needs to be uninstalled before installing pillow. For installing PIL/pillow, refer to https://pillow.readthedocs.io.

shapely¶

A preferable and very well documented package for Shapefile handling is shapely. shapely is already installed along with flusstools.

pip install Shapely

conda install -c conda-forge shapely

pyshp¶

pyshp is another shapefile handling package, which builds on pure Python code (rather than wrappers) to simplify direct dealing with shapefiles in Python. pyshp is already installed along with flusstools.

pip install pyshp

conda install -c conda-forge pyshp

Other packages¶

Besides the above-mentioned packages, there are other useful libraries for geospatial analyses with Python (Packages in bold red font are installed along with flusstools):

• alphashape creates bounding polygons containing a set of points.

• django as a geographic web frame and for database connections.

• geopandas enables the application of Pandas data frame operations to geospatial datasets.

• NetworkX for network analyses such as finding a least-cost / shortest path between two points.

• owslib to connect with Open Geospatial Consortium (OGC) web services.

• postgresql for SQL database connections.

• rasterio for processing raster data as NumPy arrays.

• rasterstats produces zonal statistics of rasters and can interact with GeoJSON files.

• sckit-image for machine learning applied to georeferenced images.