Open lithology datasets are scarce and most times they come in certain exchange formats that can´t be easily coupled with other softwares. Python has the tools to read and extract data from those files and provide them in more friendly formats as csv or xlsx. With the use of spatial libraries as Fiona or 3D visualization libraries as Pyvistas we can go one step further and process our data as shapefiles or vtks.

Storage, management and analysis of geospatial vector data as an ESRI shapefile is a common procedure of GIS and related professionals. The generation of these spatial files can be done not only on a desktop software but also by Python commands. We have created an applied example that shows the procedure in Python to create point, line, and polygon shapefiles from a csv file by the use of the Fiona library.

Python is a great tool for spatial analysis and geomachine learning, however sometimes the Windows operating system presents some difficulties to install and run the bunch of Python libraries such as Gdal, Fiona, Geopandas, Rasterio. We are aware that most geoscientists, water resources specialists and related professionals work on Windows, therefore we are always in the search of new ways to get Python working with all its geospatial capabilities in every computer. We have created a tutorial that shows the installation process of the Python geospatial libraries in Windows by the use of a Conda environment; the process is simple on its steps, however the sequence and factors related to the package compatibility are important on the installation.

We did a simple procedure in QGIS but unknown to us that extracts the centroids of lines and polygons with Field Calculator commands inside the attribute table. The procedure is straightforward and does not involve the use of any intermediate layer.

There are three essential things for the representation of geological data on QGIS: the spatial geological information, an appropriate symbology and the software knowledge. Once these three things are available, the potential of QGIS to represent geological maps is unlimited.

We have done a tutorial for the representation of synclines, anticlines, overturned synclines and overturned anticlines on the regional scale with QGIS.

In our perspective, 3D visualization of geospatial data has been a long desired feature that has been covered in some features from SAGA GIS or in some plugins from QGIS. This time we developed a Python script that converts point / line / polygon ESRI shapefiles (or any vector file) to unstructured grid Vtk format type (Vtu) by the use of the Python libraries Geopandas and Pyvista. The tutorial has files, scripts, and videos that show the whole procedure with some remarks on the software and spatial files and a discussion about the nature of the spatial files that presents some challenges in the data conversion.

If your drone doesn´t write the GPS position on the image metadata, this is a tutorial that might be of your interest. When you have the images without any location reference and the image location on another text file you can use the code described below to generate geolocated drone imagery compatible with OpenDroneMap. The tutorial shows all the steps involved besides it has some sample data to practice.

We have done a tutorial under the concept of "applied geospatial Python". This is an example that deals with a selective filtering of a determined road from a road geopackage. The selected road is composed of a group of lines that are merged into a Shapely LineString. Based on a Numpy linspace with the Shapely interpolate function, a set of points were distributed along the merged line path and later interpreted as a LineString. Resulting line was saved as a ESRI Shapefile file with Fiona.