Martin Christen Geospatial Analysis Using Python And Jupyterhub Youtube "geospatial analysis using python and jupyterhub[europython 2019 talk 2019 07 10 singapore [pydata track][basel, ch]by martin christengeospatial data i. 1. prof. martin christen fhnw – university of applied sciences and arts northwestern switzerland school of architecture, civil engineering and geomatics institute geomatics [email protected] geospatial analysis using python and jupyterhub processing, analyzing, and visualizing geospatial data on a high performance gpu server martinchristen github: martinchristen gitlab: martin.christen.
Europython 2019 Martin Christen Geospatial Analysis Using Python 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. 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. 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. Discover various python modules for low and high level geospatial data manipulation, and gain insights into leveraging deep learning frameworks for raster analysis on high performance gpus. cover topics such as coordinate reference systems, spatial queries, polygon operations, and data visualization using libraries like shapely, pandas, and.
Martin Christen Geospatial Data Processing With Python A 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. Discover various python modules for low and high level geospatial data manipulation, and gain insights into leveraging deep learning frameworks for raster analysis on high performance gpus. cover topics such as coordinate reference systems, spatial queries, polygon operations, and data visualization using libraries like shapely, pandas, and. 01:37 a python 3.5, 3.6, 3.7, whatever, won't use two and the i python kernel. 01:45 and then you activate this environment and install all your cool packages you want to use 01:51 and after that you can create a new kernel with the line above, just i python, install. The need for processing small scale to large scale spatial data is huge. in this talk, it is shown how to analyze, manipulate and visualize geospatial data by using python and various open source modules. the following modules will be covered: shapely: manipulation and analysis of geometric objects fiona the pythonic way to handle vector data.
Europython 2019 Geospatial Analysis Using Python And Jupyterhub Ppt 01:37 a python 3.5, 3.6, 3.7, whatever, won't use two and the i python kernel. 01:45 and then you activate this environment and install all your cool packages you want to use 01:51 and after that you can create a new kernel with the line above, just i python, install. The need for processing small scale to large scale spatial data is huge. in this talk, it is shown how to analyze, manipulate and visualize geospatial data by using python and various open source modules. the following modules will be covered: shapely: manipulation and analysis of geometric objects fiona the pythonic way to handle vector data.
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