Geospatial Analysis using Python and JupyterHub

Processing, analyzing, and visualizing geospatial data on a high performance GPU server

Martin Christen

Analytics Big Data Deep Learning GPU Visualization

See in schedule

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.

Type: Talk (30 mins); Python level: Intermediate; Domain level: Intermediate

Martin Christen

FHNW - University of Applied Sciences and Arts

Martin Christen is a professor of Geoinformatics and Computer Graphics at the Institute of Geomatics Engineering at the University of Applied Sciences Northwestern Switzerland (FHNW). His main research interests are geospatial Virtual- and Augmented Reality, 3D geoinformation, and interactive 3D maps.

Martin Christen is very active in the Python community. He teaches various Python-related courses and uses Python in most research projects. He organizes the PyBasel meet up - the local Python User Group Northwestern Switzerland. He also organizes the yearly GeoPython conference. He is a board member of the Python Software Verband e.V. and the EuroPython Society.