Geospatial data is data containing a spatial component – describing objects with a reference to the planet's surface. This data usually consists of a spatial component, of various attributes, and sometimes of a time reference (where, what, and when). Efficient processing and visualization of small to large-scale spatial data is a challenging task. This talk describes how to process and visualize geospatial vector and raster data using Python and the Jupyter Notebook. To process the data a high performance computer with 4 GPUS (NVidia Tesla V100), 192 GB RAM, 44 CPU Cores is used to run JupyterHub. There are numerous modules available which help using geospatial data in using low- and high-level interfaces, which are shown in this presentation. In addition, it is shown how to use deep learning for raster analysis using the high performance GPUs and several deep learning frameworks.