When

Oct 10, 2023 to Oct 11, 2023
(Europe/Berlin / UTC200)

Where

Science Park 402, 1098 XH Amsterdam

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This workshop introduces a set of tools from the Python ecosystem and shows how to use them to perform practical geospatial data analysis.

The workshop will take place at Science Park 402, 1098 XH Amsterdam. Please note that lunch and drinks at the end of the workshop are included.

Python is one of the most popular programming languages for data science and analytics, with a large and steadily growing community in the field of Earth and Space Sciences. In this workshop, we will help participants with a working knowledge of Python to familiarize with the world of geospatial raster and vector data. We will introduce a set of tools from the Python ecosystem and show how these can be used to carry out practical geospatial data analysis tasks. In particular, we will consider satellite images and public geo-datasets and demonstrate how these can be opened, explored, manipulated, combined, and visualized using Python.

The workshop is based on the teaching style of the Carpentries, and learners will follow along while the instructors write the code on screen. More information can be found on the workshop website (will be activated once registration is live).

Audience

The workshop is open and free to all researchers in the Netherlands at PhD candidate level and higher. We do not accept registrations by Master students. The workshop is aimed at PhD candidates and other researchers or research software engineers.

Prerequisites

The participant should:

  • have working knowledge of Python
  • have had exposure to the Bash shell

The participants DO NOT need to have prior knowledge of the Python tools this workshop is teaching.

Syllabus

  • Basics of raster and vector data
  • Introduction to Coordinate Reference System (CRS)
  • Access satellite imagery using Python
  • Read and visualize and process raster/vector data
  • Data analysis with the combination of raster and vector data
  • Parallel computation for geospatial data